{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 机器学习纳米学位\n",
    "## 非监督学习\n",
    "## 项目 3: 创建用户分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "欢迎来到机器学习工程师纳米学位的第三个项目！在这个notebook文件中，有些模板代码已经提供给你，但你还需要实现更多的功能来完成这个项目。除非有明确要求，你无须修改任何已给出的代码。以**'练习'**开始的标题表示接下来的代码部分中有你必须要实现的功能。每一部分都会有详细的指导，需要实现的部分也会在注释中以**'TODO'**标出。请仔细阅读所有的提示！\n",
    "\n",
    "除了实现代码外，你还**必须**回答一些与项目和你的实现有关的问题。每一个需要你回答的问题都会以**'问题 X'**为标题。请仔细阅读每个问题，并且在问题后的**'回答'**文字框中写出完整的答案。我们将根据你对问题的回答和撰写代码所实现的功能来对你提交的项目进行评分。\n",
    "\n",
    ">**提示：**Code 和 Markdown 区域可通过 **Shift + Enter** 快捷键运行。此外，Markdown可以通过双击进入编辑模式。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 开始\n",
    "\n",
    "在这个项目中，你将分析一个数据集的内在结构，这个数据集包含很多客户真对不同类型产品的年度采购额（用**金额**表示）。这个项目的任务之一是如何最好地描述一个批发商不同种类顾客之间的差异。这样做将能够使得批发商能够更好的组织他们的物流服务以满足每个客户的需求。\n",
    "\n",
    "这个项目的数据集能够在[UCI机器学习信息库](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers)中找到.因为这个项目的目的，分析将不会包括'Channel'和'Region'这两个特征——重点集中在6个记录的客户购买的产品类别上。\n",
    "\n",
    "数据的单位都是**年消费额**：\n",
    "1. FRESH: annual spending (m.u.) on fresh products (Continuous); 果蔬，肉类，水产品\n",
    "2. MILK: annual spending (m.u.) on milk products (Continuous); 牛奶制品\n",
    "3. GROCERY: annual spending (m.u.)on grocery products (Continuous); 食品杂货，香料，油盐酱醋\n",
    "4. FROZEN: annual spending (m.u.)on frozen products (Continuous) 速冻食品、冷冻食品\n",
    "5. DETERGENTS_PAPER: annual spending (m.u.) on detergents and paper products (Continuous) 清洁剂、纸品\n",
    "6. DELICATESSEN: annual spending (m.u.)on and delicatessen products (Continuous); 三明治、沙拉等熟食\n",
    "\n",
    "运行下面的的代码单元以载入整个客户数据集和一些这个项目需要的Python库。如果你的数据集载入成功，你将看到后面输出数据集的大小。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wholesale customers dataset has 440 samples with 6 features each.\n"
     ]
    }
   ],
   "source": [
    "# 引入这个项目需要的库\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import visuals as vs\n",
    "from IPython.display import display # 使得我们可以对DataFrame使用display()函数\n",
    "\n",
    "# 设置以内联的形式显示matplotlib绘制的图片（在notebook中显示更美观）\n",
    "%matplotlib inline\n",
    "\n",
    "# 载入整个客户数据集\n",
    "try:\n",
    "    data = pd.read_csv(\"customers.csv\")\n",
    "    data.drop(['Region', 'Channel'], axis = 1, inplace = True)\n",
    "    print \"Wholesale customers dataset has {} samples with {} features each.\".format(*data.shape)\n",
    "except:\n",
    "    print \"Dataset could not be loaded. Is the dataset missing?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析数据\n",
    "在这部分，你将开始分析数据，通过可视化和代码来理解每一个特征和其他特征的联系。你会看到关于数据集的统计描述，考虑每一个属性的相关性，然后从数据集中选择若干个样本数据点，你将在整个项目中一直跟踪研究这几个数据点。\n",
    "\n",
    "运行下面的代码单元给出数据集的一个统计描述。注意这个数据集包含了6个重要的产品类型：**'Fresh'**, **'Milk'**, **'Grocery'**, **'Frozen'**, **'Detergents_Paper'**和 **'Delicatessen'**。想一下这里每一个类型代表你会购买什么样的产品。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000    440.000000    440.000000    440.000000   \n",
       "mean    12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std     12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         3.000000     55.000000      3.000000     25.000000   \n",
       "25%      3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%      8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%     16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max    112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper  Delicatessen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 显示数据集的一个描述\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习: 选择样本\n",
    "为了对客户有一个更好的了解，并且了解代表他们的数据将会在这个分析过程中如何变换。最好是选择几个样本数据点，并且更为详细地分析它们。在下面的代码单元中，选择**三个**索引加入到索引列表`indices`中，这三个索引代表你要追踪的客户。我们建议你不断尝试，直到找到三个明显不同的客户。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0  12669  9656     7561     214              2674          1338\n",
       "1   7057  9810     9568    1762              3293          1776\n",
       "2   6353  8808     7684    2405              3516          7844\n",
       "3  13265  1196     4221    6404               507          1788\n",
       "4  22615  5410     7198    3915              1777          5185"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 按所有特征降序排列\n",
    "classes = list(data.columns)\n",
    "order_by_classes = data.sort_values(classes, ascending=[False]*len(classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Frozen</th>\n",
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       "      <th>Delicatessen</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>112151</td>\n",
       "      <td>29627</td>\n",
       "      <td>18148</td>\n",
       "      <td>16745</td>\n",
       "      <td>4948</td>\n",
       "      <td>8550</td>\n",
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       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>76237</td>\n",
       "      <td>3473</td>\n",
       "      <td>7102</td>\n",
       "      <td>16538</td>\n",
       "      <td>778</td>\n",
       "      <td>918</td>\n",
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       "    <tr>\n",
       "      <th>284</th>\n",
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       "      <td>2406</td>\n",
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       "      <th>39</th>\n",
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       "      <td>555</td>\n",
       "      <td>902</td>\n",
       "      <td>10002</td>\n",
       "      <td>212</td>\n",
       "      <td>2916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>56083</td>\n",
       "      <td>4563</td>\n",
       "      <td>2124</td>\n",
       "      <td>6422</td>\n",
       "      <td>730</td>\n",
       "      <td>3321</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>56082</td>\n",
       "      <td>3504</td>\n",
       "      <td>8906</td>\n",
       "      <td>18028</td>\n",
       "      <td>1480</td>\n",
       "      <td>2498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>53205</td>\n",
       "      <td>4959</td>\n",
       "      <td>7336</td>\n",
       "      <td>3012</td>\n",
       "      <td>967</td>\n",
       "      <td>818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>49063</td>\n",
       "      <td>3965</td>\n",
       "      <td>4252</td>\n",
       "      <td>5970</td>\n",
       "      <td>1041</td>\n",
       "      <td>1404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>47493</td>\n",
       "      <td>2567</td>\n",
       "      <td>3779</td>\n",
       "      <td>5243</td>\n",
       "      <td>828</td>\n",
       "      <td>2253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>45640</td>\n",
       "      <td>6958</td>\n",
       "      <td>6536</td>\n",
       "      <td>7368</td>\n",
       "      <td>1532</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>44466</td>\n",
       "      <td>54259</td>\n",
       "      <td>55571</td>\n",
       "      <td>7782</td>\n",
       "      <td>24171</td>\n",
       "      <td>6465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "181  112151  29627    18148   16745              4948          8550\n",
       "125   76237   3473     7102   16538               778           918\n",
       "284   68951   4411    12609    8692               751          2406\n",
       "39    56159    555      902   10002               212          2916\n",
       "258   56083   4563     2124    6422               730          3321\n",
       "103   56082   3504     8906   18028              1480          2498\n",
       "259   53205   4959     7336    3012               967           818\n",
       "282   49063   3965     4252    5970              1041          1404\n",
       "239   47493   2567     3779    5243               828          2253\n",
       "176   45640   6958     6536    7368              1532           230\n",
       "47    44466  54259    55571    7782             24171          6465"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 挑选47号记录\n",
    "order_by_classes.head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>140</td>\n",
       "      <td>8847</td>\n",
       "      <td>3823</td>\n",
       "      <td>142</td>\n",
       "      <td>1062</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>117</td>\n",
       "      <td>6264</td>\n",
       "      <td>21203</td>\n",
       "      <td>228</td>\n",
       "      <td>8682</td>\n",
       "      <td>1111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>97</td>\n",
       "      <td>3605</td>\n",
       "      <td>12400</td>\n",
       "      <td>98</td>\n",
       "      <td>2970</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>85</td>\n",
       "      <td>20959</td>\n",
       "      <td>45828</td>\n",
       "      <td>36</td>\n",
       "      <td>24231</td>\n",
       "      <td>1423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>37</td>\n",
       "      <td>1275</td>\n",
       "      <td>22272</td>\n",
       "      <td>137</td>\n",
       "      <td>6747</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>23</td>\n",
       "      <td>2616</td>\n",
       "      <td>8118</td>\n",
       "      <td>145</td>\n",
       "      <td>3874</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>18</td>\n",
       "      <td>7504</td>\n",
       "      <td>15205</td>\n",
       "      <td>1285</td>\n",
       "      <td>4797</td>\n",
       "      <td>6372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>9</td>\n",
       "      <td>1534</td>\n",
       "      <td>7417</td>\n",
       "      <td>175</td>\n",
       "      <td>3468</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>3</td>\n",
       "      <td>2920</td>\n",
       "      <td>6252</td>\n",
       "      <td>440</td>\n",
       "      <td>223</td>\n",
       "      <td>709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>3</td>\n",
       "      <td>333</td>\n",
       "      <td>7021</td>\n",
       "      <td>15601</td>\n",
       "      <td>15</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "128    140   8847     3823     142              1062             3\n",
       "353    117   6264    21203     228              8682          1111\n",
       "412     97   3605    12400      98              2970            62\n",
       "65      85  20959    45828      36             24231          1423\n",
       "357     37   1275    22272     137              6747           110\n",
       "96      23   2616     8118     145              3874           217\n",
       "218     18   7504    15205    1285              4797          6372\n",
       "66       9   1534     7417     175              3468            27\n",
       "95       3   2920     6252     440               223           709\n",
       "338      3    333     7021   15601                15           550"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 挑选96号和338号记录\n",
    "order_by_classes.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen samples of wholesale customers dataset:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44466</td>\n",
       "      <td>54259</td>\n",
       "      <td>55571</td>\n",
       "      <td>7782</td>\n",
       "      <td>24171</td>\n",
       "      <td>6465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>2616</td>\n",
       "      <td>8118</td>\n",
       "      <td>145</td>\n",
       "      <td>3874</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>333</td>\n",
       "      <td>7021</td>\n",
       "      <td>15601</td>\n",
       "      <td>15</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0  44466  54259    55571    7782             24171          6465\n",
       "1     23   2616     8118     145              3874           217\n",
       "2      3    333     7021   15601                15           550"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO：从数据集中选择三个你希望抽样的数据点的索引\n",
    "indices = [47, 96, 338]\n",
    "\n",
    "# 为选择的样本建立一个DataFrame\n",
    "# 使用loc定位记录，使用reset_index对挑选的记录进行重新索引，以避免乱序。\n",
    "samples = pd.DataFrame(data.loc[indices], columns = data.keys()).reset_index(drop = True)\n",
    "print \"Chosen samples of wholesale customers dataset:\"\n",
    "display(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 1\n",
    "*在你看来你选择的这三个样本点分别代表什么类型的企业（客户）？*对每一个你选择的样本客户，通过它在每一种产品类型上的花费与数据集的统计描述进行比较，给出你做上述判断的理由。\n",
    "\n",
    "\n",
    "**提示：** 企业的类型包括超市、咖啡馆、零售商以及其他。注意不要使用具体企业的名字，比如说在描述一个餐饮业客户时，你不能使用麦当劳。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Fresh               12000.297727\n",
       "Milk                 5796.265909\n",
       "Grocery              7951.277273\n",
       "Frozen               3071.931818\n",
       "Detergents_Paper     2881.493182\n",
       "Delicatessen         1524.870455\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75% percentile\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Fresh               16933.75\n",
       "Milk                 7190.25\n",
       "Grocery             10655.75\n",
       "Frozen               3554.25\n",
       "Detergents_Paper     3922.00\n",
       "Delicatessen         1820.25\n",
       "Name: 0.75, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print 'Mean'\n",
    "display(data.mean())\n",
    "print '75% percentile'\n",
    "display(data.quantile(q=0.75))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答:**\n",
    "\n",
    "ID  | Fresh\t| Milk\t| Grocery\t| Frozen\t| Detergents_Paper |\tDelicatessen\n",
    "--- | ---   | ---   | ---   | ---   | ---   | ---\n",
    "0\t|44466\t|54259\t|55571\t|7782\t|24171\t|6465\n",
    "1\t|23\t    |2616\t|8118\t|145\t|3874\t|217\n",
    "2\t|3\t    |333\t|7021\t|15601\t|15\t    |550\n",
    "\n",
    "- Index = 0（原47号）的企业类型应该是超市。可以看到这个企业的所有6个类别的采购数量不仅远远超过了平均值，也超过了75%的企业。这说明该企业的业务十分广泛，应该是类似于Walmart之类的大型超市。\n",
    "\n",
    "\n",
    "- Index = 1（原96号）的企业类型应该是咖啡馆。可以看到这个企业需要大量的杂货以及牛奶制品，少量的熟食，还有大量的清洁剂和纸品，几乎不购入冷冻食品和生鲜蔬菜。事实上，咖啡馆消耗的主要是制作咖啡的牛奶、各种香料（咖啡豆、肉桂粉、可可粉、奶油、朗姆酒、调配各种风味饮品的糖浆syrup等）、以及纸杯、纸巾。为了确保咖啡馆的环境，需要消耗大量的纸巾和清洁剂。咖啡馆也会卖一些蛋糕或三明治之类的小食。因此数据符合咖啡馆的特征，基本上应该类似于星巴克这类连锁咖啡馆。\n",
    "\n",
    "\n",
    "- Index = 2（原338号）的企业应该是冷冻食品的零售专卖商。从数据上看，这个企业唯一超过均值的项目就是Frozen，其他5项都低于平均值，说明该企业的主营业务是冷冻食品。应该是类似于鹏程食品之类的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习: 特征相关性\n",
    "一个有趣的想法是，考虑这六个类别中的一个（或者多个）产品类别，是否对于理解客户的购买行为具有实际的相关性。也就是说，当用户购买了一定数量的某一类产品，我们是否能够确定他们必然会成比例地购买另一种类的产品。有一个简单的方法可以检测相关性：我们用移除了某一个特征之后的数据集来构建一个监督学习（回归）模型，然后用这个模型去预测那个被移除的特征，再对这个预测结果进行评分，来判断由余下5个特征构建的模型对移除掉的那一个特征预测能力的好坏。\n",
    "\n",
    "在下面的代码单元中，你需要实现以下的功能：\n",
    " - 使用`DataFrame.drop`函数移除数据集中你选择的不需要的特征，并将移除后的结果赋值给`new_data`。\n",
    " - 使用`sklearn.model_selection.train_test_split`将数据集分割成训练集和测试集。\n",
    "   - 使用移除的特征作为你的目标标签。设置`test_size`为`0.25`并设置一个`random_state`。\n",
    " - 导入一个DecisionTreeRegressor（决策树回归器），设置一个`random_state`，然后用训练集训练它。\n",
    " - 使用回归器的`score`函数输出模型在测试集上的预测得分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "每个特征作为被预测列的R2评分：\n",
      "('Detergents_Paper', 0.72865518125414541)\n",
      "('Grocery', 0.60280197887845888)\n",
      "('Milk', 0.36572529273630905)\n",
      "('Frozen', 0.25397344669700861)\n",
      "('Fresh', -0.25246980768827321)\n",
      "('Delicatessen', -11.663687159428036)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# TODO：为DataFrame创建一个副本，用'drop'函数丢弃一些指定的特征\n",
    "\n",
    "def test_feature(feature):\n",
    "    f = data[feature]\n",
    "    new_data = data.drop(feature, axis=1)\n",
    "\n",
    "    # TODO：使用给定的特征作为目标，将数据分割成训练集和测试集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(new_data, f, test_size=0.25, random_state=0)\n",
    "\n",
    "    # TODO：创建一个DecisionTreeRegressor（决策树回归器）并在训练集上训练它\n",
    "    regressor = DecisionTreeRegressor(random_state=0)\n",
    "    regressor.fit(X_train, y_train)\n",
    "\n",
    "    # TODO：输出在测试集上的预测得分\n",
    "    y_pred = regressor.predict(X_test)\n",
    "    score = r2_score(y_test, y_pred)\n",
    "#     print \"<\" + feature + \"> prediction R2_Score =\", score\n",
    "    return score\n",
    "\n",
    "res = {}\n",
    "for x in data.columns:\n",
    "    res[x] = test_feature(x)\n",
    "\n",
    "sorted_feature = sorted(res.iteritems(), key=lambda d:d[1], reverse = True)\n",
    "\n",
    "print \"每个特征作为被预测列的R2评分：\"\n",
    "for s in sorted_feature:\n",
    "    print s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 2\n",
    "*你尝试预测哪一个特征？预测的得分是多少？这个特征对于区分用户的消费习惯来说必要吗？*  \n",
    "**提示：** 决定系数（coefficient of determination）, `R^2`,结果在0到1之间，1表示完美拟合，一个负的`R^2`表示模型不能够拟合数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答:**\n",
    "\n",
    "- 我对所有的特征都尝试了预测，从上面的代码结果可以看到，最能够被准确预测的特征是 `Detergent_Paper`，其测试集的 $R^2$ 分达到了0.72，然后按照分数高低依次是 `Grocery`, `Milk`, `Frozen`, `Fresh`, `Deli`, 其中最后两个 `Fresh`, `Deli` 的 $R^2$ 分是负值，说明这俩特征比较特立独行。\n",
    "\n",
    "\n",
    "- `Detergent_Paper` 得分最高，说明它与其他特征的相关性最强，换句话说，就是我们可以通过其他特征来部分决定（72%）这个特征。因此我认为 `Detergent_Paper` 在对于区分用户消费习惯的问题上并不像 `Fresh`, `Deli` 那么不可或缺，因为后者很难用其他特征涵盖。\n",
    "\n",
    "\n",
    "#### 总之，特征被其他特征预测的$R^2$分值越高，说明该特征与其他特征的相关性（Correlation）越大，说明该特征对预测输出分类（这里是企业类型）的不可或缺性（Relevance）越小。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化特征分布\n",
    "为了能够对这个数据集有一个更好的理解，我们可以对数据集中的每一个产品特征构建一个散布矩阵（scatter matrix）。如果你发现你在上面尝试预测的特征对于区分一个特定的用户来说是必须的，那么这个特征和其它的特征可能不会在下面的散射矩阵中显示任何关系。相反的，如果你认为这个特征对于识别一个特定的客户是没有作用的，那么通过散布矩阵可以看出在这个数据特征和其它特征中有关联性。运行下面的代码以创建一个散布矩阵。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TYznG8wncIETKqDCBlJK3ZqO9c7YXRPn0QUC14+7qB4j7kUuYO+JcfhTenK3y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2762b790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对于数据中的每一对特征构造一个散布矩阵\n",
    "pd.plotting.scatter_matrix(data, alpha = 0.3, figsize = (14,10), diagonal = 'kde');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting Detergents_Paper feature: \n",
      "-> Using only Fresh : -1.33785984135\n",
      "-> Using only Milk : 0.116879407346\n",
      "-> Using only Grocery : 0.681148452262\n",
      "-> Using only Frozen : -1.2061879669\n",
      "-> Using only Delicatessen : -1.45074342777\n"
     ]
    }
   ],
   "source": [
    "def test_feature(feature, label):\n",
    "    milk = data[feature]\n",
    "    new_data = data[label].values.reshape(-1, 1)    # Need to reshape X when it only has one feature.\n",
    "\n",
    "    # TODO：使用给定的特征作为目标，将数据分割成训练集和测试集\n",
    "    X_train, X_test, y_train, y_test = train_test_split(new_data, milk, test_size=0.2, random_state=0)\n",
    "\n",
    "    # TODO：创建一个DecisionTreeRegressor（决策树回归器）并在训练集上训练它\n",
    "    regressor = DecisionTreeRegressor(random_state=0)\n",
    "    regressor.fit(X_train, y_train)\n",
    "\n",
    "    # TODO：输出在测试集上的预测得分\n",
    "    y_pred = regressor.predict(X_test)\n",
    "    score = r2_score(y_test, y_pred)\n",
    "#     print \"<\" + feature + \"> prediction R2_Score =\", score\n",
    "    return score\n",
    "\n",
    "print 'Predicting Detergents_Paper feature: '\n",
    "for x in ['Fresh', 'Milk', 'Grocery', 'Frozen', 'Delicatessen']:\n",
    "    print '-> Using only', x, ':', test_feature('Detergents_Paper', x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 3\n",
    "*这里是否存在一些特征他们彼此之间存在一定程度相关性？如果有请列出。这个结果是验证了还是否认了你尝试预测的那个特征的相关性？这些特征的数据是怎么分布的？*\n",
    "\n",
    "**提示：** 这些数据是正态分布(normally distributed)的吗？大多数的数据点分布在哪？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答:**\n",
    "\n",
    "- **问：这里是否存在一些特征他们彼此之间存在一定程度相关性？如果有请列出。这个结果是验证了还是否认了你尝试预测的那个特征的相关性？**\n",
    "\n",
    "\n",
    "- 答：上面的散点矩阵本质上表示了特征与特征之间两两的相关分布，可以理解为协方差矩阵的可视化表示（不知道我理解的对不对，有问题请指正 ^_^）\n",
    "\n",
    "    由于在上个问题我们已经得知 $Detergent\\_Paper$ 的 $R_2$ 得分最高，因此我们可以观察这个特征在散点矩阵上的图。可以看到36个图中，比较有特点的图是 $Detergent\\_Paper$ 与 $Grocery$ 和 $Milk$ 的散点图，图上的数据点呈现出明显类似于 $ y = x $ 的分布，散点分布在两个坐标轴的中间区域。相比之下，剩下的散点图中，绝大多数的点都全堆在X轴附近或者Y轴附近。这个特点似乎说明 $Detergent\\_Paper$ 与 $Grocery$ 和 $Milk$ 这两个特征最为紧密相关。\n",
    "\n",
    "    为了验证和量化它们之间的相关性，我直接分别使用单个特征 $Grocery$ 和 $Milk$ 对 $Detergent\\_Paper$ 进行预测，从上面的代码可以看到，光 $Grocery$ 一个特征就能决定 $Detergent\\_Paper$ 达到 0.68 之多！这与散点图中明显的线性分布特点一致。\n",
    "\n",
    "\n",
    "- **问：这些特征的数据是怎么分布的？**\n",
    "\n",
    "\n",
    "- 答：从图中的累积概率密度图可以看到，所有特征都不是正态分布，而是右倾斜的（Right-Skewed），具有一个瘦长的主峰，主峰的右侧是一个长长的尾巴。从图中的两两特征散点图可以看到，绝大多数点都距离坐标轴很近，堆在一起，并没有均匀分散在整个坐标空间内。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理\n",
    "在这个部分，你将通过在数据上做一个合适的缩放，并检测异常点（你可以选择性移除）将数据预处理成一个更好的代表客户的形式。预处理数据是保证你在分析中能够得到显著且有意义的结果的重要环节。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习: 特征缩放\n",
    "如果数据不是正态分布的，尤其是数据的平均数和中位数相差很大的时候（表示数据非常歪斜）。这时候通常用一个非线性的缩放是[很合适的](https://github.com/czcbangkai/translations/blob/master/use_of_logarithms_in_economics/use_of_logarithms_in_economics.pdf)，[（英文原文）](http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics) — 尤其是对于金融数据。一种实现这个缩放的方法是使用[Box-Cox 变换](http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html)，这个方法能够计算出能够最佳减小数据倾斜的指数变换方法。一个比较简单的并且在大多数情况下都适用的方法是使用自然对数。\n",
    "\n",
    "在下面的代码单元中，你将需要实现以下功能：\n",
    " - 使用`np.log`函数在数据 `data` 上做一个对数缩放，然后将它的副本（不改变原始data的值）赋值给`log_data`。\n",
    " - 使用`np.log`函数在样本数据 `samples` 上做一个对数缩放，然后将它的副本赋值给`log_samples`。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 注意：这里的Log Transform是上面所说的Box-Cox变换当 $\\lambda = 0$ 时的特例\n",
    "\n",
    "$$\n",
    "\\displaystyle\n",
    "\\mathbf{Box-Cox ~ Tranform} \\quad y = \n",
    "\\begin{cases}\n",
    "\\frac {x^{\\lambda} - 1} {\\lambda} \\quad when \\; \\lambda \\neq 0 \\quad (\\mathrm{Power ~ Transform})\\\\[2ex]\n",
    "log_e x  \\quad when \\; \\lambda = 0 \\quad (\\mathrm{Log ~ Transform})\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "$\\lambda$ | 相应的函数变换\n",
    "--- | ---\n",
    "0   | $\\ln x$\n",
    "1   | $x$\n",
    "2   | $x^2$\n",
    "-1  | $1/x$\n",
    "-2  | $1/x^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
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+bsvFw76rKsDV0c8D4CYpMUEQ/j7w9wGWlpY++t9jHiB/9OoqkiDw9164++v+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7NdH0TbyVwIFBHI9qlK8FhRBKbtMVluMVFscma6StP2ADiYj/PnTg/zgeN9PHs4H27aN8HDw1l+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8MVmkYbnEdIXDPXGajkcuHkpXW65Pb0KnULeYKDTIx/XOeqMpUidA0lclkm4u1ig1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KYrVzepOElp2SHlnDZaGEOiOOXsjuj0uWGCocpEScpW08XUlOs+w2u7Sbc7NfDjZExRHPrv\nh9QemBD0OUNVbkmPMFTloT3rxwRdlVm+Q5vPOM1ww5i9QYCuSHSckLypcma7z0w5R+MDGrsDExZd\nN+T4XJmcrjJVNpgu5bjVXjfwIiQk8obKgYn8w4P1XeAbr61Ty+t89bHJj+X1Zys5/t5PHuO/+cPT\n/P7JLX7u6bmP5X0eZNQLBi8dmkCS+Fjous4o5HmyZHB0skCaiQbvtW91dc0d+tG+393sebTtkDeu\ndPmjk9s8s1zhp4/PAKK4vUpjLRjvN50sXb2O3vo+rtkb7nLqUM5pD5vT9wgVS//IOs6JW9y/V5ue\n6x2XuqUjSxITeZ3doU8QJxiqQqNosFy32BnIgMRO3+dfn95mtmLx9EKFS02b1ZZLxdJ4dqn6oev8\n/ThhfiCqCV2VPxbh12cFO30fWeaWYrOVlsOVtkMQp0iIUXk5p3Ogkb/hAffNtS7vbPRxwpg0y9AU\nhZWmw6XmkHrB4JmFKkeniqgf+N6iJOX01oA4zXh8tvTA6EAUWdpH/fogwjjhL841SdKMLxyeuOFG\n0HOjcep9xwmpFwyGfkTPjZgum1iagqUruGHCRF4URmma8ZeX2rhhTE5X+eLhCaZKJlmW4ccpYZyy\nXM/jRQlvXenRcUM6dsQvPDvPpT2HjiuE5+WcylJddP1uVzdXMjUONPIM/XjfFE+RpRtO5Vp2wOmt\nAXlD5amFyqfKFvN+RpSknNoakGYZj83sf6biRBTJWQamJtN3I5I0Y6psstHzyKkK373Y4thUiYVa\nDkNV8OMEP4yxg5iposlSPc/J9R6mKsxJvCjB0vffQ1s9j/O7Q2RJopbXmKnkHhY+d4HmMOCbZ3b5\nm19YxlA/vrXxl15c5vdObvHf/uEpvnK0ccMp82cd11JB7zUenymz1nFpFA22+6KYWeu6fPHwBIos\nsdXzyOmCxqwpMq9ebqOOuuxlU2Oj43Jqq089r7PR8bDD5DpqU8cJaRQNTE0hTTPe3ugx8GMenSlS\nzmmc3R4SJSnH58roiqBN3+jQPfQjJEl62Hy+Q2z2PLxQMFju1EDio2Jv6LPR9Zgpm8x8oAGdZRkr\nLYeeG6LrMrOGyZW2y4U9mzjN+KXPL9EoCZpkNa9TszSudD1MVeXUZh/bjxn6EesdlyBJxdlFV6nk\nNGRZwgnE3tEoGPe1tfvDu/lTjvWOy7nRhODEwvsFUJpmnNsdEsYpx6aLY+MDRZKYrZhsdj3sIIYs\nwwsTum5ITlNo2gH1vI6hyvTckCBKOL01YKlu8a3zeyiyhKrIBHHCetclpyv7Mip2Bz7NYQAI0Xbe\nEB2pWxUWDwJOrvd4d0NQEUqmyhePNK77mUbRoFbQieKUuWqOKEn5wer74sYn58t8/mAdJ4gJEkFt\ne2ejxzvrXaIk48hUkTTLUJBww5jzu0NymsIj00U0RSFDBJApssQ7m33++J1tHD8mpylMlAxOzJdZ\nnsjjhjFOkOCGMXOV3C3FvHeiOdjseoRxShiHDLzo4YHqE8JO36c1eqZWWw6NokHV0pFlie2+z07f\nJ8uEfuBquGxKRscOCMJkTJO8sDekNQxZadls9XyiNGWxZmHpCgcaBewgpmrp5D7QsPDChL+82BIH\ns5zGo8u1j/Xg+GnG7765QZxm/OLzCx/r+yiyxD/5+Sf42v/4XX7tz8/xj7/+xMf6fg+xH2VL4wlL\n6JevtIV+MklT0izD9mJObwkt7EzFZKfvkyQZp7YHPDFXxgsTDk0VGAYRq22XqqXyznqXpxar7PQ9\n7CChOQwIk5QgSvny0Qk2ex5ntgc0CgYrLQdTVfj2+SZhnHJ+d8gjMyUOTVy/1l81L5EkeGrherbC\nQ9wYPTfkzOg7jJLspq6YH4aBH3Fxz6ac0264F/e9CFniOjfVM1sDoiSj64RMl8xxI2qr57I78GnZ\n4uymyDLLk3le7bWRJAlDk9BVGS9McMOENBPazamSwZtXuuwNAvK6MtZnV3Ia3znXZLJk0igaPDJT\n5LWVDkmaMVvJ7XOYvd/wsPh5AJBlmaCoGOodT0r2cYGv0ZpddVrLsowwSWkUdDZ7HookiemPJA7Y\nr1xqc2HXJiMjTjNOzJVxwhhTVeh7ETt9j5WWQz2vU7Q0eo4Y0V99GO2RuG5v4LM7CJgs6iiKRJZl\nbHRdcprK7sDnSzcoFh4kFE0NfUQ9a9xkg7jK572KgRcx9CMsXeVy06Y1DJitmPTcCDdMkGX4g5Ob\ndJ2QgqGgKTKvXGzxxcMTvLrSGW+QjaLB8bkyXz7a4MKezSPTRd7b7OMEMV0vZM9OWWnZXN4b0rFD\nZElQ7Gp5HduPeXzu3piIzJRN2k5AXlfvmCe/0XXxwoTliU++S/ago2xpKLJEkqWstBw2uh5TJZMn\n5ssUTZXLLZu+G1EwFeJEuCi+fKE5cgeU+LFjDUqWxnrbpeuGbPd8ojhldxjghSmTpS6TJZPnl6rk\ndJU3rnQZBjGPz5aYLArTkjjN2Oq5fOHIBKZ2d99f1wnZHfrMVnIPfDPkbpBlGf/36+s8v1y9panM\nvcIj0yV++aVl/sUrK/zi8ws8tfAwdu+HgeNzZVbbDo2CgabIaEqKLIv9WldkWnbARtulktfwopiK\npbFcybPWdul7MX96ahdNkfnN769xZKrA4oTFXj8gywSN7X9/eYXdvkeYpDw5X6VRNPCimIEXockS\n2z2fyaJJnAx4aqG6j2Xjjuh5WSbo1fWb/REfAx7kPUFVZCRJfG7XXvvN9L43w8U9m44d0rFDJosG\nhqqgykJnvTvweXejjyTBo9MlijmVvK7yxlqXc7tDSqYwOvjepfbIcCjjW+ebNAcBhipjGSoSwoH2\nyFQRVZbIgHM7Aza7Hk6U4AYJ7270qeY1vCAhb6hs9X1+/NFJem6ME8YMPXHmc4KYNBVGHW4Yc35v\nyEzZvG+boA+LnwcAZ3eGbHY9VEXiC4cn7mghuOrIJMvs4/zmDRVFllhtu3S9iJmySc3S2eh6JGlG\n3lQY+ClDP2bgR6Qj69uBF5FksNYaMnBD2k5IyVSJMtElPjGn8fVn5nHCBC9MONjI40cJf35qh91B\nwEzF5N95dp4M+MFqFy9MHriF7UZ4ZFpQCRRZui3L0iBO+M75Jm07JF9XKYxoRFs9QVEc+hGnt/vY\nfoIXpZiagh+nXG7aLNfzFHSVgRdhqDLlnEaaZuwMfHRFxg5iHp0p0XFCVEXmSsthpe3gxymrHQc/\nTNnqCYe/jhNxZKp4T2ilFUvn+FyZqqXf0QLfc8OxU1Gc3n2X7LOKkqnxxSMThHHKX5zbY2AHmLqM\nHyXsDQIaBYOyqXFqe0DJ1HDDBD9MGfgRqixzuW0zXc0RJRlzlRxFU6NtB6RkVC2dnV7A//W9K0yV\nTH72qRl6rtjstkaHJk2RmCgYTJVNnln8cP53ECf0vYjaNfdJlmWc3OiRJBmtYcgXj3z27HJfW+mw\n0nL4Oz96+BN7z//8q0f4g7e3+K9//z3+1X/yhYdU1XsEJ4jpuuH4+djq3zwawBkFCnedaOTcqfLs\nUg0vTAjjRMQVpBmaIpNlYr2cKpnjw3XXCcnrKqauEKcZpzaHLFRzeFHCXCnHty80sf2EkqUxVTYp\nGCqlnMZ0OUcpp9J1IuI0ZbXlMPBiHpkpMj8yKpmr5HDDBEniY7esz7KMlh2SNxTCOH2g94SCofLc\ncg0/EnmFIAws2nbI8oR1282Nck6jY4cYmkzXCbmwZ2OoCk8vCt2NHyUkacZ3L7WoWTqHGnn6bsRy\nPY+hyizULM7vDEmzjPc2e+z1fXaHAWVTnP9qeR0/SpivWhyo53lrrcufvrcLQNFUqOcNKpZG34sw\ndIWFSo6Fep5HZ95vmO70ffaGPrMVk2EgzJpeudSinNM4udHjR4427ksK9MPi5wGAFyVjqpITxLct\nhsuyjEt7Nl6c8Mj0/sUjb6i8dLiOpkj4Ucpu32cYxOz0fbwwIUgS/DBhsmiyWM2xOwyYNk02+z55\nXcGJYvKmOuKzwrMLVX7mqTlqeYOiqeJHPkt1C1NTcIKYC3vCSEFGbPKaIvPYTBEvSqkX7s/OwJ1A\nkqQ72hxadsDZnSE9L6Tjhrx4sE6UisOnRMa3Wk0aBYPtXkA1r/H4XJkozsgbKhf3Rv79MwV0RZgO\nJFk2tsBeaTn85PFpnpyvcHFviCJLVC2NuUqO+YpFyw6QZQtVlmjaPn9wcpOj00WOThXvaLKYpCI/\nKG+o1PI6b651sf2YoqnywsHb7xFe2yV7qO27PURJOqazCtqjjIRw4LFH3dqT6yJ7o2UHVCwNU5W5\nuDdks+cyUzbZ7utYusrhySKaLHN4soCuQNbzyOnWKARPwQkidEWh7QS8sdrFCRNUVUJTJTZ7Ho/P\nlmkUfUqm9qGNjCzLeH2lix8l+8J1JUlCV2S8JPnM3gPfeH2doqHytSdmPrH3LJoa//Brj/Ir3zjJ\nN15/mP1zL3A1QDpOMnYHPlVL5/ItogGaw4A0BT8VTYHJojI2ENjqeaNnKkOWZSxdJc0y4kRELuwN\nAw5P5lEViYP1AnM1i7fXe7SdkKcWylQsnaldk51Bn4VajqOTed7bGmIZCp9brjFZMrmwO+TMzgA3\nTOh6NkmWjYsfVZGvKzyCOOHino2pKffUiv3ins2VtjuKYSjf1Z6w0nJwAqFR/WHria81gYiTlLYd\nArDTD267+DnUKDA50m2d3RbaTT9KePlCk8tNBzeKeX6pSjiKlUgyEUzddUOOTRUp5YQ+7ErbpZo3\nyPX8MW0NSUylnl2sMlfJsdV1R1Q4cU/KssG/dbzKdk8U7kenihxoFFAl+N6lFoos8+hMkTBOWarl\nudgc0nUiLF3cFwMvQlfkWxY+dhBzYXdI0dQ+Uv7h3eBh8fMA4NBEnnfWe5i6wqWmw7NLt1csrHdd\n/uTUNm6QcHK9y2zJIs5SHp0piaJFlqmO8l/KOY3dYYATRMhItIYhiiSoNPN1izSDSy2bgRtxdFr8\n/om5CpIs8fxSjYmiPhZBf/9yG9uPWW1LfOVog+Yw4OhUkVcvtxn4Eae2BhxqFOi40T3PsXgQkKYZ\nK00HiZS+G5FTZXaHAX/lxAyqIkTpSzVhKHB0uoTjxxRzKq1hyJvrXeHsFmecmC9RsXQyxCJ2eLLA\nuZ0BfS/ij97eolE0OL9ns9X1ODJd5EePTXJkqsA7G338KOH1lTZbbZ+NjsvAFxqiLx1p3HbY3oVR\niKkkwQsH6/iRCD7z4zvz8i8YKs8fqOH/kDOUHgRc7Y42h0LLA2KTna/muNJ2iVOxESmSxEbXJQO8\nMGWhqhKlGX6UUM5pvLXepV4UXeCfeGyKli10Wv/mbJMgFIXJV442yOkqM2WT/+/MHntDnwt7NtNl\nEzdIieKMM1sD6nkdVZY5tztkqmTucxps2wGmpoxNNtIMwmR0n0T7g/KeXarSc6M7Dlj8NKDvRvzJ\nu9v8wrPzn7he6mefnOW3Xl3j1/7sHD91fOYz+fnfS2S8TzdP0vetp7MsY28YoCnyPtOZ+apF34sw\nNdFpvxalnMbRqQJtO+KF5So7w4CVlgNkTBdM7FqMJsukWcZszWKxlufU1oC2HdB1I45MFikYGvOV\nHC0n5E/e2+GxmTLndwUFupTTyLKMgqmy1nGp5w3R/By5ft0IKy2H7Z5Ye0xVJkqz8Wt9lAOsN1oP\nkjRDV+U73hN6bsilPXv87+P3iM79URHEySjHSSWIMw407syl86qEYLFuMQwikiTj8rbNpb0hXpzy\n5HyZqXJubET0wQbUS4cnqBeGrHdc5is5VtoOp7f6dOyQ6WKOjZ5Hzwv5F6+sIgE//liDnhMzUTAo\nmhpdPUaRJdpOyLFpmT98Z4uNrsuBep6OE6DKMrIMZNLo7015dknYdVesW9OXL+7ZrLZdNEWiUTQ+\nUcfA2y5+JElqAH8bWL7297Is+1v3/rIe4lrkDTFhSdJsX8bKhyFOM0EjsQOiJOHstk3JVGkOA752\nQhsvFle7LXldoVEUh5dM6pEkKaosI2Vwfm+IMq7gM+YrFoen8rx5pcf/8+Y6R6eK4wJns+fx6HSJ\nsqWx0XVxgpiaJZxAJIRIT1GksaPZZw2vrnT4/uUOXTcmpyvsDgIkqc98NYemyLgjPY6mylzYHvL9\n1TayBAVDI4gSLu3ZFA2VqqXz0uEJvDBBV2QWqhZL9TxntofkDZWcJnRCV0Msa3lRoF7NWeo6IUkm\nxtZtO8DQZE5vDcbFz3rHZWfgs1SzmLxBQZQkGXEq7pEsyzgxX2G77zE7cpe5erg1NYUsy3h3s0/X\njTg2VbzOdrVkap9Jnced4qrFqB3E6KqEoSoUDJWNrtDeaYooNGQJcqrCZt+jVtBo2iF+EKMrMgNP\ndOeSNOPIZIEwTnGCmFdXOmyMAqXDNGW16bBQzzM0Yr7+zDyvrbQ5tyumTWGccGFvyHI9jypLnN0Z\nEEQpXSdkpmQiyxKXmzZ/cW6PoR/zMydmOTwpMkSemKsImkQ5x9CPKBgqkiRhagrT5c+mUcLvndwk\niFP+2ucWP/H3liSJX/254/zUP3uZX/uzs/yTnz/xiV/DpwmKLPHUQoW2EzJbMTFVBU2R2Ox6rLVd\nNrseLx6qjycT5ZzGS4duTPM8udbjwq6NHcRMlgzBksjgm6f3eGKuzDCIkSRYa7viECoJ58WqJYxN\nFmsWTy1WOLXdxwsT3l7vU7E0ZBn2hgFRmqIrCkVTxCioskQpp6HJN5+2WJpK2w7o+xHbfZfTW0Ny\nhsLnD9SZLBl3vY4fnSoiS6CNrge4o9cyNUVoH9MM6z4yXDm7PaQ5DJAk+MLhibueSJVzGpWcztao\nWEkysQf33Zj56vUTumtRsVTe24yoF3RqeQ0nSNgdCH1lnKZ863x7fI07vYDFmkWcZaiKzFrboetG\nPLdcZW/oIyMKeSeMmSqbuEFClsETcyWadsBUycTQFOzAZaPrcniycFO2kuPHXNqzcYKYLM04Ol3i\n2PQnE49xJ5Of3wdeBr4JJB/ysw9xD6EqMk8vVui6QptzO4iTFE0WAvt6wcBUZb53uU3HCSjlxOGo\n44S8vtohSjKW6od11R4AACAASURBVHmKpsax6RKPzRRZrFnsDn3e2ejzzTO7DL0YRZFYrOf5xecW\n6HohJ9e6vHq5Q2sYIAFdN2CzK9zcpooB02WT33x1jY4T8vhcic8tVXGihBNzZT53oH5f2yB+HEjT\njMsth7W2Q5ZlSICpyOSLKjOVHJf2bHpuhCSJDXS+mhsFzqYEccJyPU/V0kCSqOWEwcJqy8UNEyxd\n4cVDdVRFxg1jCkaeH3t0kumywUrLZbacI4gFbfLqxvDcco16XqdeMHh7vUfLDsedxzTNxrSq77Sb\nPLVQEQfdUVfJjxL2hgEdJ+Spxcp4s7raNW7bASdHLkHPLFbRFJm9gXAkW++6t8ic+HC4ochDqheM\nz1yXuutEnNruQwZfe2KGnhex1nEpjTpmRUPlYCPPTt8nTrNR40HHCWKqBZ1qQadm6ay0HDpOiKWr\nrLZcLrVskjTj0GSenUFAPW/w9mafidF06PRWn7fWuxxs5Hlhuc7ZnQF2mFIr6CiyhBcmtO2QA438\n+Lk+tzPk0p5DJaex0rLHXeFG0aBRNHj1cpuhHzNdNu+bLu0PA1mW8duvrXF8rvRD+xyOThX5W19Y\n5p+/vMK/+/zCPmOW+x1ZlrHWEUX7Ys36RPQFfpTw5pUuYZLy1ELlusNdNa/vE3ov1fMMvBg3TEbu\nngG7g4CSqd6UAtV3I660ba6MnFj3hgFLdYvzu0OcMKbjhhxqFJgqGcyUc+R1hbc3ekyVDOJE6PXO\n7Az5/ME6TTvg5fNNCqbK9Mi+/p1NUQgdnSqyUM3xzmaf7b5PvaDv25vPbA/oOCGLNRGIXM4J4yVN\nlXhrrYcswcCNiJIMXZa50naQkFBkcMKEpbp1W7btmiLT92K8MECW2Be+fju4GqAexMlHzsm5l5BH\n9+PV0NGPgiAWR+/5qpAUNIcBOU2hlNMY+BFuIDRGHzxbXWo6JGlGGKVMFEwR3ZFTCeKEOMkwFBGz\n0fMiNnsu6x2Px2dLvHWlS3MYsNKy8cOYo1NFVtoOcZwyVTI5MVehaQeUcyLX8WqT1A5iVlvu+L1v\nxlaaKBocnRIBq1GSsd5xOTJZ+ETOhndS/FhZlv0XH9uVPMQtcbvBVwM/YqUpEpuF7bTEv/+5Bd5c\n63J6u0eWZaQphElKlKQs1Sy2R8nMqgyKLMaWp7f7rHc9wjihbOmoskTVEnbJp7YGvLc1YKVpMwgi\n+m7EpV0bXSky8GO8KEFCHIC7ToQTJNi+EOEXDI3jc6VPZeGTpBmntwaEScKxqRKaKu3jvG71Pc5u\nD4jSlNmKyXbPEw5xYUJpRmF7ENB1QoIk5cm5Mqosc3AyT8FUmSyZfOVYg6EfsTsIeO1ym6KpcGZ7\nQMXSCRMhbj+zPcDSVYaj++CNtR7OiNI2XRahtXlTQ5UlnluqsVCziJKUzZ5HwVTJG2KTkmURVLvW\ndug6Ed+/1GGnH4yF6ANP5MVMFs3xuPtaDPxYuA1lMPBi5qs5qnmdnhvedgF/M7y70Wfox6x3Xb58\npHFH5goPMvwoYbPnYXsxyxMWTTvEGbkpFkyV43NlZEkE6tbyOkmaIUnvBxyWLZ2dvkfXC+m6Irvj\nSsvh0ZkiXTugNRSd6sONAmkGC1WLQ5MF/Cjhf/qLizhBws7A5ycenSana0hSwlTRZGtEv8sbCtOj\nza85DMiyDFOTcMOIoiHoNVefhSTNxiGOVw0UPqt4d7PP2Z0hv/pzx3+o1/ErXz06Nj/4/b/zxQfG\n/GCz53FhV9CdZEn6RAIVu26IG4qD6O4guK29+chUAU0VeTl7g+B9F6/SjaMe3tvqUzQ1iobKo3Ml\nFqsWrWGAE8REiTAjkiWJqqWzPfDRVJnZSm5UVMm8tdYFJJI05UijQDJa55NMuH3+tc8tEsQpmiIR\npxl9NyKvq1xuOhiq8v7eMAre/ubpXRZqFroqUyuItdzSVZww5guHJ/iRYw2adsCFXaHv9aKEWl4n\njEWWkBvGbHS9ccPtgwjjFG/0mfa8u1sTcrpy39nsPzpTpGJplHLaR87uOjZVZFV1RINUkjgwUaCc\n19nouFxu2vT9mNmyyU8/ISj0ThCz0/d4b7PPattlqZbjaydmiJKUMBbFKpkwlDg0WWClJWhoO32P\nvKmQ11Xe2uiy1fFY63jsDHwemyljFEUz89RWn6cXhUNgGIv7q2SqY7aJFyaiYXsTHJjIk2YZpZwm\n8udGrIFPAndS/PyRJEk/nWXZn3xsV/MQHxnnd4b03IiVls1CzUKWFTa6Pm9c6XFhT/j753QFRZJ4\nc63Lq5c6PLlQIowTLnZczu867E0H9L0YCQlZkrD9iCBKKeZUynkdiZSNrstu3yclw48T3ChhbxjS\nKOqUTIWuF/PS4QJ9P2Kr5/PIdIkXD07cdwvTvULbDljveOwOPfwo5dyOTZwIl7bH50o8Pltm4MV8\n69welq5yqGEhyRKrbQdJknh1tUs1p2NoMs8sVdBVQRVbrud5ZrFK3lA5uzVgZ+BTNDUmSyZVS2el\n7dD3QmRJdHvqeZ3VtsN8tUyUZpiqwjCLaNqCm9sahjy7VCNJMjpuyJwuqHYTRYOOvb8wEUJIk39z\napedgY8zolhczXtoFA38KGGhlmO947LedZmvWCzWLeYqgtIkSxIzFbGgPbtU3XcAvltcLXYUWR53\n1T4L2Oi6aCNaStHUODBhcXp7gBskOH7MQlUcTPwo4cKujapA0VTJGxqPzxb5zrk9Tm70CKKUtiPo\nCU3bZ/U9m91hgKYoTBR0yjl9RD+R6DgBQZQSpykdJ6DuaLhRwucP1giTFEsXVvWyJPIhLuwNadrB\n6FoUDjWKREnKme0Bhibz9GiioMgSx6aL7A19Fmt3xoH/tOEbr69jajI/++TsD/U6CobKP/jaY/xn\nv/0Wv/XaGr/0+QfD/OBajcMn5Rxay+sUTJVoFPJ4O5AliSOTRRRZIoyFAN7Q5Otysy43Bc2t54rp\n0LHpIsdnyyzWLH7n9TV2hwFhJEyMdFXmOxeaSJJEZsFzSzXcMObMzoBvvLaOKsvYgdD+rLRcMoRJ\nDZJocK11XNZHU7OdgY/tJ9QLGhf3bMIk5XCjQMXS6LkRxdxV3V7GiwfrnNkaYKoKSHBsqkTeUOmP\nihZZBlURa/NVw4JTWwP6bsRG1+VLRxpoisxmzxPuZBMWli6m1l034tAd6mLuZ6iKfE8K8p2+z+nt\nvmgsphnrXY8jkwU6dkCcCFp5XldRJIndYUDPCXn5YpMgTnh7rY+pyZQMhZWWw1LN4o21Hu9u94mz\njKcWKvzII5NYuspWP2C2bDJVFBPEvKYijfadesFgsZ6jaQc4obBLX2k5HJsu8p3zTc7uDKjldb7+\nzPx4CvfBQOxroauCrvfoTOmenA3uBHdS/PwK8PclSQqBEJCALMuyB8uD8FOOgilS2A9PFpmr5mgU\nDXpuRNcJKZkaBUPlxx+dZKvn8vpqByeMubDnEESwPfA4MlVgomCw0fXITLi45zH0ErpuIOw47ZCj\nM0UsXaGc1yDLsAwFVRIhl5oidAiyDG9e6VLO6cwvWzy/XMPURdK0GyXkdeUTvdH9KOFK26VoqvfU\nsjNKUr53qcWprQHTJZPze0OiOKNoqmQZFHMq2z2f6ZLJt87t0fdC/DhlObN4bqlKMjpAdtyIOImZ\nrYhFMoxSzu/ZHJ8tEcQpGUJ39eaVLo/Nlfj8ATGBMTSZIBIGA7oqs1S3Rl0XlUemi1iaTJikXNqz\n8aOUkqXhhjGWoVK1NLZ6Lqttl3JO44uH62z1fTa6LvNVC1mWmK1YPL1U5fTWgI2uR2sYcHZnwLNL\nNZ68JhfkB1e6JEnGxeaQxbo1dg1crFvjA0maZry31ccJklE37O6oCSfmy+wNA6ojHdmDjDTNWBmF\nHB6o52/591QsnZyhcHy+zPPLNWFZa2q8fLE54u/3eXapyuWmw+7A59R2H8ePaDshS7U8ewOPjh0i\nARVDo2goIEnEXkZOU5AkEaq4UM3TtH12hj5dN+Jww2KxJqgrj08XaQ59KjlhyZumGXsDoR0IooS1\njiiAq5bG88s1rnQcXr3coT/aJOcqOdY6Lqam8NhMad+hIMsyzmwP6bkhR6eLN7QF/rTBCWL+4OQW\nP/3EzCcq9r0Z/sqJGX771TX++z87y08fn34gQi2nSibygnhu7tQwZXfg03XDUZjvrY9DfiS0lbIs\n9rjP34Gj5e7A573NPpoi87kDNQ42CkyWTCTE5C/LMh6fLRNE6dgZbrvnU8uLZsSxqSIXmzZhnFI0\nVfSrYcVZxsm1HmGSUslp40Dr5jAgiBMSOSWKEzZ7Lps9F2vUja8XdLb7HrsDMbW9sDfkcKOAZyUI\n30hxwJNlieeWa8RJihsl7PR9poomhqpwdFq4tUoS1PIal5rCAe7EfJlgpCNMs4y2HfB2mIw1w7Ik\nmqpOEI+DQIM44bHZEj0vQpGl8Xex2nLY6nks1KzbKiCSUSTHD9vp7W7ghjFhnF63LwZxgoTEVt8j\nTQXFsGxpHJ0u8MRsmbfWe1xuOXhRgixLpFlKTpP4l29v8e5mDy9ISLIMc0RrK+RUOl4EGWSp2IMW\nankmiyZ7BZ+5stDspFnGMIh4aqHM8wdqLNXzzJZNvn+5I1zhFAlJksbF+9ntAZdbDntDEap97fd4\nO/ik7bBv+8qyLPtkVEgP8ZFwbKrITClHTldGmhCHy01hd3x8rsxS3WKhlme941I0VPwwJW8oPL1U\nYcmxONiw0FSJQ5N5Tm10RFimH5ORMVuxeHerj6HJFE2VONFZ73rYfoivq6RJxlzFpOcJx7idnkeS\nwlTZZKKg8+hsmR9c6TLwok+c539+dzjWnJRyogi8F9jp+3RG1L5hEDNVMmgUTNpOSCUnrH9nyiYb\nXRdTk8lpKpoiUy8YHJ0q8vRilTevdPjj93bY6UdE8SgYTVXQZIkky0jTjEzKaA4D3DBls+NRO66x\nUBMZSqtth7wuRs2yJOxQwyQd5zid2h4wV3n/894eJYb/qzc3ROp30eDYVIk4STm/a7Pd93luqcrB\nRoGVlkOcJEwUdFSZmwbtNgoGO32fiYJBlmVc2BO2nBd2bWZGBgg9Lxp/B2sd966LH02R9zmKPcjY\n7HmsjA47+od0CCcKBl84PIEsSeOCUldldEWYF6QjMxRdlTi/O+QHKx0kBMXlakbIkwtlHD/h8bky\nPTdkreMSxCmKIvPIVIFDDdE0qVoazWGIE0Ssdz3mKjksQ+VC26X/xiaLtRy//NIBel7E66sddvse\nF5pDeq6wOi9bGnaQsFy3RtQLn0peZ7vnj2huEZMlQ9AmR3DCRHSlEYeez0Lx88fvbmMHMf/e85+8\n0cGNIEkS/91ffZyf+mcv80//7Cy/9gtP/rAv6bZwNy6RfpTw3mafLAMnSHh26eY6p9WWw8U9G0tX\n+NyB2h1Tbdt2SJYJalfXDalaOgVDZb3j0hlZIG/2xHOmKhJxko31dqWcyjdP7/LqSodKTuHgRJ4D\nE3meWarxe29tsNX3KBgqlVHzJIxTpotCBySRsViz6PkxBUMdU+bWOh5rHZcDE3mutF2eXqwSxCmT\nJZO5iokfp2PjGhDTi5Iij+l5aZph6SovHKjx+mqH/+OVFcIoRVEkvnJ0kr4bst5xudgaQipxeLLA\nTx6fZrpsUhmFMmuKPP5bc7rCVs8ffxY7ls9i3eJS0ybLhNHLhxU/QZzw2kqHIEp5dLb0QO0RdhDz\nu29s0HYCnl+u8qUjk8D72llZkliuW9h+zGOzJSRgo+fR92PKlsbhRh4niDg6XWSqZLLbD7jSdtjo\nCCOMnK6gSzJdN+Tb51rULMEeGfghUyWDgRfy/UutkTlWRtN2STMwVBlJkvn60/NMFA3e3ehhB7HQ\nh1bzPLNUHetuS5aGpSsEUcabV3o8uVi5Z+esjwN34vYmAX8dOJBl2a9KkrQAzGRZ9trHdnWfYsRJ\nymrbQVcUFuv3jqMsSUKrcRWbPY/Nrk/XC1EUiRPzFSRgZ+AxUzaZr+R4Yq5My/Yp53T2BgHvbQ1o\nDgN2+j72iFecMxQkhAi/60b4cUJBVxn6IUGc4ccxByYsOl5EOacx9GOSMCXNYCqDIMlI0ozBaCz+\nSfP8r3JtFVlC/ZBpQZZlrLZd0iy7jW68RtnSODCR5/BkgaKpsjcMeGrknT/0Ik6u93BCsfnMVHKj\nBSHD9mNymkKUZiJvKRbTnrlajtmCzo8cneS9rT6ntvskaYamyhxq5MkbKkkq3NiutF1mKiZxmnJ+\nZ8iRqTw9VxRhPTdibxiQJEJfUcqJgMueG/L2Ro/LLZudno+uKpia4Hhv9T2iOONS02a967DZ9bjc\ncvny0QYz5RyPTJeo5rTrRtTH58ocmSqMP+eKpbPZc9kZxLx6uc1To4Uwpyv40UNL66swtPcPUbeT\nZ/FBzvhVM5S2EzJXyYmsp75PTlM4PFlgbxgQJhlRnFEraJzZHo442BkLtRy7fZ+iKWhuUZpxamvA\nXDXHI7Ml3tro0XVCDk8ZyCOazMXdIZsdh9PbKtMlk5Kl8e5Gj+2BTxgmtN0QWcp45WKLlw4JJ8LP\nH6xzbnfIlaZDM/HJAF1VrtM55DSFgqli+/G+oujTiizL+JffW+XwZIHnl+8fg4EjU0X+wy8e4H/7\nzmV+8fnFWxYFDzIUWUKRxeHb+JBnr+2IQ7k70rIU77D4WaxbdJwAXZG4uGsTxClHpgrj6c1aW+TT\n+FHMTNnEDmIenytydttmveXwJ+/t0PEiCrrCl49MULY0msMAJ0iYLuWoWCpffbTBpZZL3lR5ZrmG\npiqj7rtCKafTHAbMVnK0nYD5am7cbLm2qBj6Eastl2r+5lP187tD3t3okaTC4fNb55pcag7puhHP\nL1dHrJIu2z2f7b43ClxNeenwBAcbhbGWTFdlnl+q4YQxFUvjuxdbnNkesDxhjc8wjaIhgppvY79w\ngmTMgmjbwQNV/LhBTHMoGoOrLZcvHRH/v+tGZBk07QBFFlNOL0wY+EKf1RoGzFVzrEQpjaJJ34tZ\nrMq8uiKmM0GckikZDdNgrm4yGZl0vZByTmOz77Hd8/jzU7uc3howCGIm8gY/+sgkizULOxD3QpKl\nnN8dUrY0gjhFHYWjfvloY5/h0LNLwi2w54Z4kWhkHZ0q7nN9vZ9wJ2XZ/wKkwI8BvwrYwP8MPP8x\nXNenHisthysjW9mcrtzw4d7qeaTZKPjyLkeCC1WL11c7JHHKVtfjzStdkjTjN15ZZbPn8dhsiX99\ndo/WMCBOMyaLBhkZthez0xPVv3CkKaDKElMlU7iCuBGvXGwy8GNAYq5sUtRVen6IFyRULI0XluvY\nYTI+iF3L8/8kRKnX4shkgaqlYd1kcnEttvr+OC9AHXnngyhYT20NiJKUx2ZLWLpK0dT40uGJcdYO\nwMFRdtG5nSErLZuzOwN0RSGME56cr+CEsbCo9CJ+69UryLI0cmFTIU15Z72HG6Ys1fO07JCOE2Ko\nCnldYa6c49h0kYWaxbfP75Gm8Cdvb43oG4KX/ZVjkxRNla2eRz2vESUpR6eKzJRNem7IlbaDFyS4\nQYIfp5QtndYwYKvnoUgSUZay3fOI04xhEFMwFIZ+xOOzguH6nYtNDFXhueXqvsP4tf/9zGIFTZFE\neK4f07ZDZis5XjxYJx1ZaD4ETBZNnl2SyeCuneuuNUM5sz1gveuNNkuJrhMiyxm7Q4831kTw4irg\nRykvHa5TzWuc3R0SxiluGLNULxDEgibZsUNMVeHkWo/Dk3maA4+2E9JzIyZLMu9uDlio5VisWxRM\nsRHHWUbR1MiPtH2yBKttlwu7Nud3h5iqwtefnaNRMK67BxRZ4oUDtXGa/acdP7jS5b3NAf/o547f\ndwnof/fHj/D7J7f4r37vPf7w7z445gd3gqsUtKEf0/iQKePBiTwX0ozySG93u4iTlI4b4oYxfpQy\n8BNsP6FlBwyDiH/76Xlmyjm6Tsjp7QFvrXeJk4yZssmlpk2cZHhRwqWmQ8lSscOU09sD4hRqBTE9\nzeki5uD7K13mqxZtO2DoR8xUczyzUGF7tAZXLF1QTlVhib1QvX4PPjfSDG/2XM7vDAmTlEONAkem\nimMTpUtNm7+81BYH6wx6nphqWZrCoYkCy3UxTYrilHQUuqmNWChhkvL4rGAgXKUC5jSF5QmLNBX7\n9EwlN6aAnpivECXpba0HVUtjpmLihgnLE/ePZuh2rr9RNHhqoTKiRkq8tdblyFSR+WqO3aHPoB1h\nBxF//M4WGRITeZ1Dk0UsQ6Ge1wnilNYwoO9FfP9yh2pBY6aSww0TVFnCixM2uj5TRYNnl0Rj9p+/\nfIm2E9J2QrYHHrIk0y+GTJdNvv7MPOWcxkrLwY8TOk7Ar3/7Em6YcHiqgKUr+7KqAPK6SsEQOjhF\nlpgoGPTckDfXumQZPL1Yva/cWe+k+Hkhy7JnJEl6CyDLsq4kSffPX/KA4dour36DB2N34HN6xIfN\nMm5aLHwwRPCDqOY1DjXyvLHaQZIk3lzroSsSfS8izTL6ToSqyiRpSscOKRoqlZxGqGbkDJWSpXGo\nUeByS7h+td2Izx+oMvCjcXib7Yf4eZ2VjuAUW7rE43Nl5qoWUZIxX8uNx5+3y92915Bl6YZZNSCo\nCBtdl6Kp0Sga+76Pq99TGKf8xbk9LuyKbBNhCy6YoDc7yHfdEFWWx/kLk0WTYRAxkTdoOQGvr7Y5\ntTVAliUOTlg8s1jh7fU+Az8iSlO+eWaXyYJJGKdMFETKc8USHPCLezZrbZe+H3Fma4AfCz72IzNF\nzm4P6bsR232fRtHgl19aRpIkBn7EK5favL3RHzm1GcxXLQqGymJdWMSqskwlL/PWWo+Zisl0yeTE\nQpmDEwXqeZ1vX2gShCmlHOM08htBkiQWaxZtJ0ST5fGiJ8vSmFP+EALVvC4cE5s2M2XzhjzpvaHP\n+R2biqXx+Gzppofl7b7PD1a7+HFMNSdyoda7Hooi4tK9KEWVxUT4/I4NZGQIh8eSJBHFwsDi9ZWO\nKIiihJmSiSRJhClM5A28MEYiY73r8Mh0kScXTHKa0PNd2LXx4pRfeHYeN0z4zvk9tkc6sqtWr6aq\n3PSZkSQJTfls3B+/8coqJVPl68/M/bAv5ToUDJV/+DOP8p/+1lv85qtX+A9eXP5hX9LHAktXb0uX\nUM3rfO5ADXjfdbGev96B1Y8S3l7vIUkSJ+bLnN4e0LFDdgc+k0UDXVHwIqG7i6KU715oCj2GJhOm\n6bjIPLU1YOBFDAPBg64XNEEhLxkossyZnQFHp4pMlgwaeZ3VjocdxNhBzIGJPHEic3HXpj9yZAvi\nRPzbj8cNvaYdMPGBpqulC83wRtej50RommBKbHQ9VlsOs5UcsiRhqLJodkkSP3V8iv/3TJOJosGB\nyQJPzFdIM6EjenKugqZJnN+xkSSJdJR/vTvweX2lgyxJZJnQAVm6QhALdsm1uN1GiCRJ48LqfsF7\nm312+v6H0vwlSeKrj00hy4Ji+a1zTS43bYqmRjknmCVvXunStEMGXoypyhRMBUWSeeVSmyttm64T\nEqcwXTJw/ZhjUyWmSibvbvYJU9FsXaoJ+nKaZkSxyOjTVZmCIRPEGYosE6cZ232fvCEcRE9tDfj+\npTZX2g7bfZ+Vls2LhyY4ud7bp3vb7HlYumB3fOFQHV1TWGu74+984N1fAdZ3UvxEkiQpiADjq6Gn\ndxbl/hBjLNXz5HQFQ1H20dTuBCsth5NrXXRV5rHZEhtdEW527eFod+CT01SOTBVxwngkRGMcmvqj\nxxqc2xkycEMW63nsIGaxblHKacRpQpTA0akCzWEwMk4I2B36KJLE4zNFzu4MkZDouAH1gkFeN1io\n5Xh+ucZiLUecio7MwI/GoZv3G87tDNkd+EgSvHioTmPUHUmzbCz4bdkBcZISJRkdJ6Sa//Dv7Mik\n0Mx88UiDjiMcWWqWhqmJRO5LmdBnmJrCVMnkb7x4gGMzbd7d6BPEQrjZdkKcMEaRZSaLxth1a7vv\nM1+1cHYGLNSFXfm0lNF2QgzNw1DFNMnUZIJYCECTJKPnhKiyNO40Pb1YQZMF/eHc7pCuq7HZ8eg6\nEdW8xhcPNZir5Hh1pY3tC+vS7b7HQt2ids3Gv9py2O57PD5bHmfOVCydHznauO+62vcbkjTjzTUx\nkW0NA164gZB6re3iRwk7/YQDE/n/n703j5UrTc/7fmc/dWrf7r6Tl2vv3ex1ejQaj1oaOZJlx7IM\nxXacGAmQQLARC04COECUAE6AGDYQG3JkKzZsS7IcW7FgS5FmRjPj2aeX6Z1s7pe8+711a686+5Y/\nvrrVZLPZ3ezphd0zz19kkbg8rDp1vu973+f9PbctdmgyFDIqRVT2Bx6DIB4TFi1NZqmsUrAMvDih\nNfTJGgoVS9je7pkRAXMZTcELEx5ZKvPcWpu9vocTxjw4X2aqYHJsMockQRiDqSvECdh+QkZXePr4\nxDhg8fdf2uJr5xsA/Jn7Z8iaKk6QjLMqfpS103X50rk9/tpnlu9oKPij1J++d5rfPbrB3/3yRb54\nz/SPtFU1TlIaA4+coY67Ixsth8+s1m5a0w67LCDW3v2eR98VSfelrI6lK6zUs6wd2DRGBE1Flnlg\nocyjy1Uu7A1IEXkn37vSZDJvsDRhsdP1CKIUN4goZFQm8ia1nMH+QFjCem5AGCU8ulTn0eUKf3R2\nlzBO6DkRjh+TwChMG4axAOjkzVvvu5PTIoBaU8S8aMcJ0BSZ87t99vsiCHuqYDKRN6jlDH723ik0\nWWbgCRiEhLDOtWwfS1N4fac3LijGqQhVdoOY1za77PVd9no+j69UqedNpoqZj5z49WHr0MrWGHjA\nux/MqlmDKw2bvCHs8x0nQJFknjk9iSpLmJrMXs9npmxRyxl07BA3iOi7EYamYNsBKRLzZYusKbKY\npgsGQShy2C7uDkmSlHO7A0ASh7KZAs/cM4UdhKztO3hRzEbbJqvLJGmGWl5nqZ7l7E4PRRZUzyRJ\nieI3t/9eDEBpMAAAIABJREFUKDpMQz9idSKHPnLXTJfMMQFwtnx32RDv5Kn7D4DfByYkSfo7wJ8H\n/qcP5ap+RPROvvbJgkk6K7CSt8tFWTsYcvXAJklTdroelayGHyYs1yz8MCFBfJm2Oi73zRT59pUD\nmp7PVtfnmVOTnJjO86+e2+D17T4gUTBV+l7IV883UCXRil2uWVxrOcxXLII4xdQULu3bnJzKM1UU\nONtzuwNkSdjzfvmxBTKayl5PUHSePFLj6oHN9aaNock8vlK96w5Ah89aSXozkKz8lgpF2dLJmxr3\nzha4Z674tp/d4RyXpsgsVrNUc8b48PTGbo//79Vd2k5ALatTzZuoisxCRXRefvLEBKam8PTqBE8d\nqTPwI2w/5De/tUbfiXhtq8tMaYrmwCcIE+wwYqZocWwqj+PFVLOCvnVpf8hG2yGJUwoZlThJ+N6V\nJqauMFM0ud528MKIUsZiqWqx1xP2qOV6jgcXyth+xHevNJGklDgRG+kXrre53rK5vC+CKh9bqfLw\nQnnsCW8Nfb52YZ+OHXL1wOYvnpkfV/Y/TQvZhyWJN+/B29mLJgsmXSekMCI6vVUdO2B/IA7EaZqy\n3naQAEOWyOd0NEVGU2SKls5UwWS5btEehsyUTNaaNqYic2Iqz5F6jlc2e6iyxKubHfb7YkbH1BTu\nmy2yXLP40tk9XrzeIWeqLNctVmo5wjjFC2NmSyZbHY8gjnltq8vVA5swSjg+PeDzxyeRpYhzO32q\nOeOuew58lPqX318nTVP+yhN3L05akiT+l5+/hy/+n9/i//jSBf7uL34y4Acfhi7s9dntCtvXoW1a\nkrilh13N6ay3xKuKLBEmCU4QszKR5cxSZfz3ZkoZGn2PS/tDVEXYyU1NYXo0p3Juu4eE6NZPF032\newGtgUNGV3h4FBr9ylYPU5WJYzFbK0kS11o2x6cLPHNqirPbfVRFbJiDKKVXCVhvOZyeKfDwogg/\n3+o4tO2ApVqWgqkhSWKe46HFMpNFk8m8yXbXpTn02ek5zJQM/vhsh822y2TR5KdOTRHEIWVLp2Rp\nHJ3I0xz6bLddvn6hgSyLQM6VepbZkkXLCajlRC7R+d0BuioTJcnIKvXREmA/Cq3Us2x33Pe88Red\nfUHT7Tghr272yBrg+DFPr9Y5PpWnNQwoZ3UmC6YIvPWFi6fR86nkDRYrFos1i4OBz07HRZYlhoEY\nRZBlxAxoLIpViiwcGh0npJLVuHIwRJElXtvs8ZU39pkrZXjm9BT5kaXNVAQFbqPjcGJa2ODdIOJL\nZ3dZO7CZKAhS3KE0RebeuburG3eoO6G9/Y4kSS8Cfwrxnf+FNE3Pf2hX9mO9bX5AY+BxvelQzxtM\nFkyqOZ3mUFQXLu4NmS6ZeOcjLjaGhHHCUjXLTxyvs950mCiYdJyIc9s9VidydJ0I249xgohSRkOV\nZXpOgB/E2EkiMn78mMm8QZyKbpGlK+z1HNwwoZKT+OzxCQxNoZzVOT1b5Kkjdb5zpUmapviheKg1\nhz4d28fUVLwwvus2PSenC5Qsjbyh3TIP1BzZCnOGytOrNdGiv80G9XrLGacaZ3SFibyYsYmSlP2e\nx3rbZrvt8NBimaemchQMlcmCwZmlCien3yTGy7JEMaOx3/MwNRVFCahkNf7jhQPCOMENIlbqOR5Z\nrHB0Isc//fYal/ZtypYmKnWyhBPEPLxUIYwThn5EkoqqZKPvESaw3nI5tzPA1GTmypkxKSyKU6YK\nGTbaLgM3ZOCF1PIGZ7f7TBYMZooZlms3QyA6TsjagY0iScyUTKIk5YfMcvuRkixLnFmq0LYDJgpv\nX12v5nSWa1l6bsBaczhOhY8TQQF8dbOLIkt4UcxCNUuSiAM3koQfJyQJo4OPweeOT/DAfBGQ2OqK\nzc92x+P1nT6VvMlW26ZtB+z2PIqmyk7fY7pg8n998yot2xfUqjhFUwS8YnUyx9ntPmGc8tU3GiRp\nStZQmS9ZqIqEpWsc9Hyut2xkSWz0fpTymd6qnhPy28+u88V7p5l7m7mLu0lHJ3L81SeX+L+/c42/\n+tTSXWcrejclSUrHCW5LqXyvimLxfEzSlJPT+dEMjYaqyLSGProqkzc1CqbGZ1frgKBbWpqKVVEp\nW8bo5yQ0hwElS2NhVCDTFHlsr07TdAQpSZgpZfCaMYYmgDRenOA6MbafsDcQoZZ+JAK1s7ooXO52\nPc5t95gcdWgPIwsAotFBzA9TVEXCC2PO7/SRJAk/Epk+212XsqXx6mYPpJRazqCY0ShZOoosArvt\nIMKPEloDn+9ePcBUVUGdVGU0RRwOL+73adk+UQIzRYuMriJJkNUVDFVhdSJHaxiM3g+f17a6HJ3I\n31W2qNspTVOaw4CsodzStd1sOzQGPktVi2rOYLGaHc8Mv5ucIBqH9WZ0lYcXKuOgcMtQeOF6m4EX\nsVTLjuM60jTlyoFNcxhQMBUsTaHRd+m5IkNqppwhTU2uN0VHR5Nl6jmDn713Gi+MORh4bPc8rh4M\nyegKbTsQiGs3QlUlrjWFQ6BtBxybLHAw8DgY+CSJz8X9AY+tVPnBeoc3dga0nYBCRh8DDu523Qnt\n7Z8C/zBN01+/4bVfS9P01z6MC/uwtd116ToBS9XbW0juRl3ZH+IEMX035Mmjoouy1XFIU5DklMm8\nyTcvN2j0PfpuRBCKoUNTk9nvezT6LqoiOkM5Q+XhxTLFjEqapnRsUVkeeCFSKglbnirhhCKoarfn\nYmoKfTeimjOpWDp5Q+XzJyfJjfyhL2926LoBYZTy+JGqYPnv9nljp8+xydzH/fa9rRRZettNyNWD\nIdcObGQZHl+pYo0e4LfTITEoShJUSaJtB7y03gHEUOh+38ePU7pOhIywpRVNnaEf31LxStOUjbbD\nvXNF8qbCXt/j4l4fXRGDEw8vavTckDhJud526DkhHSfgwYUiL6530VWZqaJJPW/QHAYoksT9cyW+\nUT7gelNQ3AZ+wFw5y1TJHHccnjxao+eGnNvpEafw+naf//ZzRzlSy3Jpf4gsSZTekkey3XV5cL7E\nXt/jJ49P3HVUl0+CsoZ62+eQF8Y8t9bmcmOAoSrMljLU8ybFjMbZ7R4HA5/LBwNW67mxha3vhjyx\nUqXrhthBTDmjkSCgFJttm8eWK2x3XdpDnzd2+2x3XJwgpp7TeWmzS8cJOOj7TJcyfPZYna2Oy9qB\nSBYvWRpz5Qx+FOOFCZcbNlGS0Oh5PHutTdXSOTmT58HFIl13GjeKmcqbqLKYLXx0ufKpHKB/r/rn\n37vO0I/4lZ88+nFfynvSr/zkKv/2xS3+9z+6wG/9tUc/UdX5N3b77PU8dFXmySPV9w1aOT41yrYb\nHQQOZ30OEdiyDI8uV8kZ6viwUcnq3DsnsnsO51he3+7RGgboqsxnjtZu+c6vtxyuNIZEiQANLNcs\nNjsuK7Use30PL4yJU0F0TFIIIkFcc8MIN0jQVRFerisCIvDTp9/MalJlmZypYmoyYZzw2laXZ9da\nWIbKo0sVfue5dVLE2rM/iiSo5wweWarw6FKVteaQ2ZLJbDnDv395Bz+M+YPXdqhlDVZqOUqWzsAL\niRPww5TpYoYkhV96dI75SvYmu+sD82UKpoYbxTR6Ps1hwEZ7n88eq9/1pMeL+wO22mKO8skj1THo\nJ4wTLu4NAIHdfvIOUf2aIqOpMkM3pDnwWaxYPLZSJU4EjfD8rvjZzaHP0YkcXhjTcQLcICZJUtwg\n5oo9pDd65pMmPLJUoZY3mStbdN2A1cksE/kMTx2pYuoqz15r8gcv7+BFCbIbMVvKMFEweHatJZwG\nRZXnr7VJgJwusp2yuoofJ9RHB9UoSVmuZ8n0BATp6ESOvZ5Hc+izWLXuCBDyUepOdv0/DTwiSdLf\nS9P0X45e+3ng1z7wq/qQ5QbxOFzLC5OPHOWZJCn7AzFQ9lbc67upktNx2u7Y/nJ8Ks/qRI69vsd0\nyeT1rR62HzFwRTbPMAjZaNmcmCowVTTxwgRJkrjesjENBTeMODaVZ+CEfOtyi47jc2yiQBgnZHWR\n7OuGIa2BS9+PKWU0posZNEVmr+9zuWFjKBJnVqr4YUJvVO1STYnZUkZUp8IYL4zZ7Xl0neCu/TK8\nVYcVjCQRwIMkFWjqGzduQZTQtkUlr5zVqeYEUefC3oCFG8AOpYzGdEHkJ+iqTNcNaTmBQFeGIp+g\nkFE5PplHkkR42GTBZL/vcXqmwIXdAV4U03MTHpgvESYpy7Usiixx/2yRCzt9NEXmyr7NYiXLkYks\nS9UsS7UsraGPH4k29yNLJb59uYEdhFzZjzjoB0zmDfxRp6iS1XnqaI3Xt3vs9jxkSUKS4MhEHkNT\nuLA74JXNLmeWK+N7N0oE0ej0bJHJgslGy8HQZCZvA5j4sW4vP4pFqvYNm6IwToiTlKyu4gQRTiiy\nOmw/wh0Fi9ojJP0jSxV0VaZ3NESRBJb2D17dwdAVDE0hCBO2ux7//pUt/vjsPl0nQFdkDFUmq6sE\nSYqpKnSGIX6UMFM0+exqnW9eaDBwQ4ZBxJ97cI5G3+PVrR5fObcv7v+MSIW3vYh6zuTC7pCmHbJQ\nzfKf3D/D7zy7zrNrTZaquXcsIHzaNfQj/tl3r/GFk5M3dXvvZhUtjb/++VX+1z98g29eOuBzxyc+\n7ku6RV4Yk6aM59sO5Y6e4UGU/FAdaVNTWJ28NerQi95cI/wwRlPEAP9hAejGZ6A3CgmVJJATiNMU\nGYkkSbkyIrslI6xwxxEEtYcWy2iKhxuKkNCKpSNJ8BPH65zf7aOMsuB+6tQkeVPjasPmYCjmc4d+\ndNNatVTNjkI0Nd7Y6fPHZ/e42hiOkPiC9GkHMWGc4IUxbhBzaX9ILWfy4GKJ+xeK7Pc9rjVtVifz\nvHCtRc+LMBRRnBv6EZcbA05MFbh3rkDfC4niFC+IeWOnz7WmzURe5NtVcjqWobJQsQiihNe3e+iK\nzOtbPZ46eqsD426SN8Jpx3E6wqSL11VZIjvKUno/gcVbHZc0TRn6ERVN59WtLk+v1sdOmWJGhKEf\nqefo2AHPX2vx0kaHKE64bzbP9ZbLTs+jN/rsc6bGwI2YKcnU8gaFjMragcOlfRsviHlgoYwfpJi6\niizHIrogo7HVEfeCIkuj3J+EqaLFfXMlHl2u8L2rLQxN5omjImj99HSBYkbjiZUqfS/i2iiYNk3F\n9+9Gu+fdpDs5/DSAnwR+W5Kkx4C/wa2W10+EVEV6M1zrI/qSxUk6fhBdaojKgSzDEyu1Wx7Y76QT\nUwUWK1kMVSZNU/puRNZQmCllmCllROUlrfH18/sUMhpDLyZvpFxuDLlvroimyMQxTOQNrh/YtOyA\nhJTtto0ThMiSRNcNmC1nyGrC8tJzI3puhB/GBJFAVZ6YzvFbz27QGgQsVsW/fbUwYL6coedG405K\nzlDY7Dg4QcxM8f0juz8OHeK5LU1lr++x1XbJGiLY7bC69+pWl54T0nVDShmNrY5DzlR5bauLqckc\nncgRJSlZQ+HC3kAgo2eLyCMa0NF6jv2+QFTudgWw4nDBvHeuyKk4z3PX28RJSl7XKGbEAn+kLjIi\nem6IoQlgwU7X4fxen5KlcWapTHPo8721Jtttl6ValkeWKlSzBtMli9ZQ4FfdMOLZa2Kmp+dGLNay\nPH20xgPzRXKm6DK07YBqzsANxEKfpuAFMQVTYLSTOGW2mEFXhOf8MLRTW5Tf0cbQ6Hs0Bj7zZett\noR9+FNN3IypZ/UeiU+AEEc9daxPHKadmCmNrQ97UODEtwkfdIKIx8PnS2V2KGTHnJUviXpVHw6gA\nxYxGFCe8uN4V0BNd5YH5Eo1QbFJeWve43hwSxAnHJ/Ocni2iyhKr9Ry6IvGdKwekKbTtgJ2ug6zI\nnFmu8NljdSYLJn//Kxe53rJxgoidjos7yuoAMFSJ/YHP1YMhV3MGj65UCEfJ5ZKUctD3KVrvHRnc\nc0M2229afT/J+u1n1+m5Ib/y+U9G1+dQf+nxRf7F96/zv/3ReT5ztHZXoeo7tsDpwq043RNTedZb\nDpWs/qFsqFdqws2Q0USo+PeutEhS0Y25ERCRJKmInEhTgjDhoYWywAiHMXlDZWMUe+FFIoetNfR5\nY7eHG0aEUcpe32OmLNwWTx2tMV/Jcnq2OC7ibnUcGoMA0pSspmArMY8slm+i0U0UDEqWRiGj8fUL\n+2iyjCIJm1otZ1LJmvhRjAR89XwD24/4xsV9vne1yRdOTfJz982w2XbpOxF7PbFWxUkKpERxTM9N\nmS9nQEoJk5TtrkvB1Pn6xYPR3JJHIaNiBxHrGzZ+mLCjKzy2XEGRJPpuiCwJSurhs+3tdHhYBDhy\nQ27Qh6kkSTm/18cLExYrFoYqU8hoNxWpJEni0eUKzijT706Upilro0DXgR9RzQmq36E9eH+0D1Mk\niTRN2eq4fH+txdntPnPlDLIskcuoRJGANNXyOvOVLCenC2iyxCubXWZLwgmy3/O42rRBkug4PmEU\nU80aFEyRHbXWGNB2Q5IE4iSmOSK2HpvKc263jxeKtV9CrOH1vMFEweTVze7IEpcSk6LJ8ke2v34/\nupNPSErTtAf8nCRJvwZ8g/eCr7gLpSli8H7oR1Q/Ao/pK5td0casWqxO5t/0D4+qP2/Vft/jubUW\nM6UMDy0I6thhjkySpFxpDNnuuqwdDEhSiQfmizx1VMyjTORNXt7oMlXMMJHXWW85tOwAS1foexEn\npgpjmkySwMHQww8TTFUgmZMkoZwVXtX5cgZjd8h6y8bxI3yJMcf98r5N1w4hTQnjlLO7PbY6LrOV\nDP/lU8vjxfG17R45QyNvxrfNM/qwFMYJez2PoqXdcYcNhEVovmzRc0NaHWEDsP2IMEkwZPGlDiJR\nBeq5IZosPrsLeyFLNZHRc2K6QJrCRtvhxFSBrhtACooCJ6cKTBUzgMTlxoDWMMAyFJ46WhvjRDfa\nNs9ebaEoEot1i4plsDqRY6aU4cvndnnheoe9nocXRrTtUHjPDZX5SoaLe0O+d7lJYyBQqyen8zy0\nWOHn77Np9V2atkJGkwmiGEWWGfgRSSLutXtmS2x2XF7f7pHVFe5fKLJYzRLGKboqjz9HVZbImiqS\nH1PM6DdVQ95pSYpiUe1LU+h7IU8eqd3052ma8sK1Dl4YU83pPLjw6QxaBLGwhomYzYpHz4aeG44P\nP8C4mPD18/uc3x0wcEPunS8x9ATaVgIKlo4fxWMbRnPo07Z9em5Ec+Dz+HKFR5YqfPXCPpf2h6Sj\n7/NKPU9GV2GUpL7f9ymYGkGUYOkKr2/36bvRyNKQ5w9e3WHoRzh+hKZA2xWHmZWJHFOFDGVL4xsX\nGlxpCJT27/1gCxB+/0JG5fxeH1mSbuoevpPe2Olj+xH7fY9qVr+rNt53or4X8o+/eZXPHqvzwHzp\n476cO5KuyvyPP3OC/+Z3XuL/fWmLXzqz8HFf0lgDL+JwGR14N+N086b2jojhH1a6KnNiSnTwtrsu\nYZzQdUJ2e+7Iciy67hM5g9ZQQEkOAUbPX2uTpAmrE3kkSTwHeo6gbJ7b6Y277scmClRzBvPlDKdn\ni0wVRUHqMJIhjGP+zQtb9NyQfEbjnpkiD1Wy1HJvFgr6bsgfvrYDwOeO13lipUoYJRyZyLFQsXho\nsUzeFMW7r5zbY6aUwY8SDgY+ViKx3nQI4oSposkL19vMli3xXlsasiKz3nIoWipqH9ZbNqqioEoy\nUZySM8TzaKfn4ccJJ6by4861qcmoiswjSxUaAzGTem67j6JIPLFSfdsD63bXHR8WTfX9hcSHo/Un\nTlLumSm+awG6ZQfsdj3xb2rybWffFFl6z0Wd/b6HrsiUszqSJHJxdnsuC6M4kPlKZhTCm/DC9TaX\n94csVkV8hCxJ6IqMpStkNIX5SoahFzFdMpkuZjBUmcmiwWIlw7/4/gZXGgPObsGpmYIITJdlXtlo\ni8gT4FTBpOsF9N0QN0yQkciZCm0noWCqFCydalbnGxcaBHEqcgkNhTQVUAeRbShACpah8uB8ET9K\nP5L99fvVnRx+/sPhL9I0/bUR/OC/++Av6aORORoi/LB1iK8F2Ot7rE7mKWQ0bD9kqZa7qULghTGy\nJPGVN/bY7/lc2Ouz1bZ5bXvARMHgZ+6ZIm+o7Pc9trsOO12PkqVzMPSJk5QX1zt8+/IBXphw/1xR\nUGbqEi9v9NBkiY2Ww2TeZL0tDjNtN2S2mCGMBB1KVaCeM0X+jxuyOpnnl87M8cpGl0EQ8s3zB+z2\nPVqOz3PXW/TcAFMTrevmIGC34+KGET9Y77BUswjClHJG5NMs17L8xLH6LQn1H6bOjvzViizdgiR9\nN4kuR8rz10UlXldlSpZGLWfc9H+4d67ITtfl+FSOH4zmezRFIhwdirww4eURxthUZWw/ZmPgMPQj\nzu/0eXS5yj2zRVbqWdp2wGubvXH2QZKmXN4f8OJ6h2JG4/65Mj93/zRBJIYQv32pyeXGENuPKWZU\nFsoZtg694HGKE8YjX7iAWryx06c59Pna+QMkWcbSZTKazKnpArVChocWSsyO4AdrB0OeW2sjS7DR\ntvmT8/v8woOzPPGWQ4okSTy6VMH2YwoZlTRlPPz6VmLejTqkJrlB/LYVviRl7BF3PyEDlO9HUZzw\n/PU2jh9zdCLHbFlsOpZvE9InyQIdn9Fk6jmd9ZZN1hB2uCBOeX50kHz2apNvXWkyGFWRs6bKuZ0+\nQZyy0XTwooRjEzmqOYO+G9AYeGy1RYW8mNVYqlnkDI2nV+v84HqLvb7LTm8E8IgTrrccVEWi0XNp\nDkOGToyuyvz8fTO8tNklTBOqOYOcLp5XDy9VsP2IKBEJ5su17LiC+G6ydAXbjzBU5RMNSviNb1yl\n44T89z99/OO+lPeln7lnivvnS/yDr13hzz44d1NW3cep6ZJJ3xM43RsLBh+1pgomL1xr03UD9roe\n00WP56+10RQZfyJHmqaQgJRKvLTe4cvn9shoClld5Z6ZIn4srHlhlFDPm7hhQhgh8l8ASZbQFJln\n11pjq2nZ0vh3rzRY2x/SdUOmizqfO15ntmRRzmq8cL0tiGpxwk7XI05Svnp+nwfmy5xZrvDqZo8k\nFUGqWV3l4l6fKE4JopjHlitcOxjScgL8KOa5tRaTBZOCKbKAiqaGockjOqiMqUps+A4yEo1BwFwl\nwxdPT/GFU5M8u9biesuGFM7t9Njpeux2PZ46IrD+iiwxXcyM0dDxiEj2dnu0G18z9fd3D+73PdrD\nABCHqaMT7zyPnDfVsVuobL2/DX00+ny9MGan67IzOkw9vFimnNW5f75ElCR07JArjQEzJXF43Wg7\nXGva9N2Qg744IF49sJnI65xZqvDEkQpDL6JpB8xXLPquyGBsDgKaw5CuEzD0I6IkZa3pMF822erY\nXG4MiJOUjK5ycX/AYsUiiGJIU/woxtRkLE0hn1F5aLFEY+AjKxJJlHB8Kj8uOLSdgFdf2uZg6DNT\nNHlgvkghc/ceeg51J7S3/1mSpEngzOil59I0/fyHc1mfHimyxGLVYq/vsVTNstURyclwc2X8YCCI\nJ7IErWHAbs8FUlrDgKsHNsu1LGe3eyzXslxv2ViawsnpArKMwA6P5niCKKVt+8hynpKls9l2KZgq\n9YJBLW8wDCJhdUvEAGQ9b2AHEXlTYLIHfowbxnTsAF2WKVsa982V2GzbnJ4pEqdge7For2Z1ihmV\n45MFipbHRtthoWLRcwK+fHbAbClDOavzV55YRFPkcf7LR6XDENaUlLdpsN1WGy2HS/sDFFkMlCqy\nRNZQeHjxVu9qwdQoTIn/V0ZTWW856KqCH8aikt4YjkPcpkomkixxaV+EkLadgCP1HBttB0sXXP8k\nSfnB9Q7LNYtXt3pkDRldkZgqGizXsuOqUnPo0/ciJEmintd4cK7Cds/FMlRmyhmGXszDi2XSNOX1\nLRlZFvMff/LGPo4fEyUp5azOkytVUkniCycnCaKEL53dI6urfPdKk52uix+JbB9VFovcWw8/IEJe\ni9Yh2pr3RLCSJIlHlsoMvOhtFxNFlrh3tihscR9DKO5HJTeMcXxxuGvZwbvOHy6Us7yxMyCjKeQz\n2igbA5zRIdIPE4ZuyHevNllrDNnoOIRxim8HbHUEnlbTZAqmStnSyeoq620RYJwzVRJSCqbGT5+a\n4vMnJokSkShfywm7DoCly8yWM3RsnwQZ1xfBiXGS0hj6rLccMqpKxdJZrlp4Ucpu1xVV7Mk8zWHA\nSj1L/T0OBN87W6TtBORN9bakxbtduz2Xf/qda/zCAzMfaifiw5QkSfzNnzrGf/7Pnuf/+cEmf/nx\nuwPTrSnyXfGeHq71lq7QHPo8e7XFxf0hpqZQtXQ6jggtjUlo9gKyI9TwVselmNFRFXG4matYJFLK\n0I2YL1soo0PPoVpDQdoKowRFluk7IVGSkDdVHl2qMl3McHwqz4vrbb5zuUkYJZSzKk4YoSJhqAqN\nvuhIxaNFsu+G1HMGGU1lpZ6jZGk8tFBmuyu6/+e2e+z1fPb7PvOjAs1+3+N60yYMBWghjFNSSaLj\nBEhSSjmj40UJGV1lupjBHlFHk1gM5xcyGgcjUu2hVifyqLJM3lRv20Gp5w3OjAJn389sDYgMOlWR\nRJbfe+hOmJpwZMRJ+r6K5uLw2Gav55Gkqfi8DGGbi5I3NyeKLHMw8NntuWiKzGeP1Wn0BWFtuyOQ\n4yVLw49jVEXm9GyB5VqOlze6BFFCN4iZK2VouwGDIKKWFfsGsc5ElC11fD2SJOOHIZYmRkG2Og52\nGBOmKYoijyA8KaWMTnPgs9fzOD6Zxw1jPnd8gsbAxwtjbD+ibfv03ZBqVh+Hmt7tuhPa218A/i7C\n7iYB/1CSpL+VpunvfUjX9qlQGCfMVyxmShnWW854bgLe3JwDdEcDjn0vomCqVLI6uiIqNjlTFa3N\ncobdrsdixSJnajwxqprsdF3+9QsbY/zisakanz1W543tPqdm8sRJjocWymx0XK43hzx9tMbewKdg\nqiyKsdg1AAAgAElEQVRUsixULJ691qaU0ZBk2G677A98DoY+37x0QDVrsFi1CJOEqaJB3tSYLBic\n3x0wU7b4Tx+e4fzekFpO5/K+TccNyY7ayOkNQaEftU7PFNgZzdHcSZWy44iKUJzA6mSOOEnf04a+\nljf4wslJ1g6GDLwIWZbI6DIlyyKMU5aqORYqWWwvojH0aA5EV6qYESnOkwWTnhOiKRJbbZdiRuT2\nnJoucN98iUeWymO7WHPoc+9cEccXhC5ZlqgVDPpOwIX9AW/s9Hh0ucJPnZxktpShMfDZbDsCrSoH\nnJ7N87P3TJNKEgcDj5c3Olxr2li6wtWDIeYofHWpalHLG8iSxNGJHK9tdXH8iCAWi9r9c6Vb3tuD\ngc/BwGeuknnHyr6hKhi52y8kEwWTiU/4jMe7KW9qzJYz9Nzwtt2eGzVbznB8NHjtBTH3z5cYeBEP\nLZbY7nhUcjqmoZAzNCRSSqY2BmxUczqOHzKR1UlyOo8tVVlvO9RzIrQwihNsPyZriE3ZS6MA5c8c\nrbHTE4n2373S5BsXGxQMjXtmCzh+B9sXc2gPzZeZL1uUrT5pkjJZMLlvvjhC2qY0hh5bHZfjU/mR\nTeK9SZaFJeSTrL//lUukKfzqM5/Mrs+hPrta45HFMr/+9Sv84sNzd/Vg+sehk9MFWsMDQVXsuNRy\nYu0pWBrzFREYeXyyQGPg0ffEvO5UIcNe38NQZU7PFOh7EQ/Nl+l5ERP5W/OwqlmdOE6ZKWbouAFT\nBZOZUoZjk6KTOzV6ZmqKPELhe2x0xAFlrigOU7IMK7Usuz2PME6YK1ujdS7DtaaNHyV03VBYr1s2\na83huADoRTEZVSEdzTEvVCwWazmmCsLa13NDoiSlmtOZKWWIExG+fWpa2PeOTxWwDJW9vj8emj9U\nRlc4NfPuIJC3O/TEScqrW10cP+bUTOEd501FdEWdJE3fsyNEZKXd/s+jOOHC3oAkTTkxVbhpXXR8\nMS/tRzF+mLBQsShZOtOjOZxDnZzOjyIuxLyPG8TU8gZz5Yw4oOZ1gijhnukiq5M5Ts8UKVs6U0UT\nSRKZO0EsukfljEbRVDkxLaBYHcfnjb0BmiyzOJ3F1BSag4ByTsdQZUxVoTEUEKYTU3mmCibn9wY0\nhwFtOyRMEpZqeSbyBl6Y0Bh4FDPiM97pueiqwqmZPCVL41rTJoyFg+FuizY51J3Y3v42cCZN0waA\nJEl14KvAjw8/t5ETRDx/TQyrJ8lhcnHKUk3ceDfm+MxXrNGgmz62eMyWMixULH7mnilW6jlypsZz\nay2cIGbyhjyQc9v9cQv1sZUyDy5UMFQxbC7LEvMVCz+K2eu52H7EQiXLT52eGldW4iRlIm8y9EOq\neZ3f+f4GWVMRw/SKghckREmCpsgYmkLHDskaCr/4yDyz5QyaqvK54xNsd10yuoseS6xO5VBl+WO1\nIZiackebrEMt17OEo0Pnci17R5CGe2aL3DNbZO1AIMmPTuRu2SB87sQEBwNfhIZpythv/MV7Jvmt\n76+TNRQkUoIgpWhqdD1B0bl/voQkSbSGARISy7UstZxB1w2J4xQ7CNnrJWQNjUv7A5DgZ05P8fSx\nOkMvYrfnstfzGPoh982VqecNnrvW4mvnGwI7GsQUMyKobqpgsFx3OLNU5vSoA+NHCTsdl+2uiyoL\nGl1z6N/0GcdJyuvbXZJE4L3fOsvzY92qO6F+mZrC8sgiuVITOSGHB4NK1hh72aMk5Z65EposiUU0\njJivZNloOxiqwnrHIYhj/tJjC/S9iIv7A17d7NJzBTa/MfDoOiGaIvPgYolTMwWiOOVfv7BJRlNo\nDH2eXC6z1/Oxg5j7Z4s8tCTmBsbD3FFMxw7xw5jG0McJIlbrefxQ0KR+VDbOL210+LcvbvFfPb38\nie9iSpLE33zmGL/8m8/xu89v8F88tfxxX9JdJVNTWKrluN60kSUoZXUWKhbL1azoeqQpR+pZjtSz\nPLRQJmuobLRsvn2lSd5UeXG9I7okEuPh+ThOuNayubA7oJIVEKNaziBnqnzh9CR+mDBfzhClKWe3\ne1w9GHKUHPPlDJ87XufLZ/fotW0OBhFHanlkSZBYy5Z+U2Hy1c0u15o253Z6PDhfZu1gKCAmEizX\ncmQ0lRNTeVRFFtTInssD82UWKxaPH6nw9YsN/F7CY8sVFmpZJvImU0VzPDMsSWIeRlckFFlmuZZl\n9gPcH/TccGxl2+o475oZpMgSygfI7Nrteez1xD4sZzg37T1KlihyeVFMc+CjazJnlsu37C0MVeHp\nYzUu7Q/J6iI/sZzVqWR1vpUX+Ymrk/lxp7M19Flr2hydyLFSy/LyZlfce4AdJDhajK4qBFHERtsb\nHcJSHsyX+R9+5iTPrrVYb9ksV7O8vt1jvW1jairPnJ7isaUKX7vQ4FuXDvCjhO2uyzOnpsgaKi+u\nt+nYIR075IkjVZ45NYUsSciyJGx5DQGkEDCeWymJd4Pu5PAjHx58RmoBd+eR7i5R343GcAM/TjBV\nBU1VWKpmbxnaNTWFh0ZD3RlNoXV+nzBJuH++SO6G6vljK1WCEbYYBG2j5wYMfDEgXTA19noe2x0x\nfFmyNCqWxvev9tjuuKiKzJGJ3E0t5Y2WzTcuHZDRFe6Vi/yVJ5d4eaMjBgvbDo22ixNE6KrMdsch\nSlIagxQkidXJ3Dj3JUlSVFlGlUVFe7qYIU1Tzu/26bkhxyfz7zgHcreoYGo88kPiGd966ErTdPyg\nMzXlbTdBWx2XnhtycX/Aqak8pazORMHgta0+Q0/lXz+/yaPLFeHPNhTunRUHmLPbXb57ucXVgwFu\nGLE/ELbGmVKGg6FPJWeM4QKyJFHMGFzY6/P8tYim7aMrMlsdl6MTWX75sQVKI4qPKkvj+zRvanSd\nYET5EUnghnYrzU2WQFcUvORHZ3P7UetIPceR+tv/WWPg0x4G5AwFVRaIUxlhTUxISNKE719rMfQi\nvDDGMjT+9L3T2CMcbdZQxXNKkVlr2thBjCpLFC0NWZYoWxppmlDPG+z2fRbKFgnw4GKZQkbjSmPA\nZtvFDWIEOgNMXWGxkqUx8PCihPmCOc7E+rQrihP+9u+fZapg8je+cOzjvpwPRE8eqfH4SoVf/49X\n+YtnFu6IVvqjoJValoyuYKryTYeL41NvbgJ7bsjl/QF5U9jMFioie+9wfZSAr76xz9oID21qClcb\nQ5pDH1WReWC+xJGJHNNFkbd1qTGka4ejANKYy40h9ZzB6mSOz6zW0K/JDDwBGOi5Aee2RTTCjetU\nGCdstB2aQ5+15pCTMwXRVSpk2O12qI0IZFlD5TOrNRRFIkmE1flrFxo4fswjixVWp3JjCh6I9U6S\nxEzPSj1La3RACaJD6MEHc//kTZGV5obRuPv1UapgasiyoKG+1eIv1l5YOxiS1VWiWFjx366uao3I\nnDeqkjX4Mw/M4IUJpiaenUGU8Mpmh92uj6FJJKnYQxRMjZypMvQjChkRfO5HCUgIYJGSslCxuLgn\n7NNPHamx1/fY6IjCmKWrdJyAr188IAUqOYODvocXCKvjSj1HMaPTscNRFqR8037WUBUkSfyfP8oZ\n7zvVnRx+viRJ0peB3x39/peAP/rgL+lWHQ6KfdI2U/W8wVTRxI8STkzmscOIgqm9K60oiBOWq1ma\nts/Lm11OzxRQFZmCqaHI0k2LzZXGkJKl8/hylTPLZV7b6nEwCFg7GLJSz7HZcfjulSbdEUVmdTJ/\nC4bx0E8/8CLypsqpmSKrk3nWmza/+e01cobCtaZD2dKpWDptJwBZZr6cYaGSHfvwD4PcJAmmi+LX\nQz9iu+MCsNa0efgTcPj5IOSFMZf2B5iagiLBtabDRMHgvjnxUDukqt2I6ZQliamCiRvELNVFaNxS\nNYsfJlxvO9hdYZk8NV3gM0dq6JqCF8Y0+h5N26PjhOz3ReXp5FSB+YpFOauz03VoDQOxcZXEwuoE\nIrPJ9iOWqhanZwpYukqS3r69X7J0njxSI4gScoaCLN96Hx/O8vS9kMr7HAz9sd6/ypaGqojK7lLV\nYrqU4ex2HyeIGbgxm22X1tAniOGg7/Pdy02iKGWhmuHPPzKH48eUszodOyBJ4dxWn44T8O3LTeoF\ng5lShr/61DK6LPNvXtwY5VxIHJ3Ic25b/N3DTcixqRw5Q1zPpb0Bs+UMR2tZkKVPFPL+h9G/+P46\n53f7/KP/7KE7xt/ezfrVZ47zi7/xfX7r2ev815898nFfzl0lWZbetaOxdjAcU1vPLJV5dKlKkIhs\nvdbQR5Yl/sMr2+N5voym0LYD3DDGHCHoD8NAz+30uLw3HIdkpmk6tp3bfsxnVuucnilgagqtoc8/\n/tYacZJSsrSbDj8np/Nc2B0we6RGKauPbbgPL1fQNRlFlnh5s0OSwmTB4CeOTeCGMa9tdtFkmY7j\nMVeG0mjYfTiaBZwsmOiLMjtdlwu7whaWNzUKGWHvD+MEZdQ1+GGkKTJPHKmSJOkHPhuYpiIuxAtj\njk3m33YvWrQ0njxSuyVvKk0F5vz5a23cICGIQu6b0971God+hKZI4wOEJN2895Mk6LrROPw2o4s+\nljyieG6P5ixXJ3OkCcyWTM7u9MTBVGLcpdruumR1lVpWxwvEwI7jxwzSCFLxs5KcwKQfFs2PTuSY\nLpq3HHwO34czyyLe4OMaeXgvuhPgwd+SJOnPAZ8ZvfRP0jT9/Q/nst7U4aBYMCJMfJJsA4os3TSI\nmTXf/u0O44SBF1EwVFRVpmzpvL7V5cr+kKymcLVhM1fOsFC1OHZD0FqSpCCJjXaUpLyx0xepvKbG\n0ckcE3kTw5FZO7BRFQldFb78t+Kml6pZ9nouLSdgvWXznStNqlmdx5crqIro9uQMUQ2uZUVVwQ0T\n5irW+MAD4ss5P6oofO38PvMVi5VaFstQRrMpd+8X4YPWtaZNY5SS7YQRW22XF9fbDDwxWPrsNUFS\n+8zROkujRebBhTKSBA8tlallDeYrFl6YULR0Ck7ARMEkTlMMTcxwdFo215o2P7jWwY8S/CjB0BR0\nReLIZJbHVkRo6YvXO7hhTEXSeXSpxG7P5765Ipf2h1i6wkbH4blrbSYKJkv17Dtmqay3HDbbDpWc\nPu5UvlUfFUnxx7pVlq6yOpHn3E6Pph2yXM9zpJ5jr+9SyWp88yIihFhJWK5ZhEnMn5zfY6Fi8dee\nXqZgivkzRYK1A5ueF/CNiwccDD3itEDBFJuzJBXdJEmWWB5toOIkQQL6ToihK5iqMu4MPrJUwQtj\nnr0unuX3jAJxP83abDv8/a9c5CeO1fniPVMf9+V8oDqzVOHp1Rq/8c01fvmxxU/Vwe7DVpKk9N2Q\ny40BBUPDCWLiNB3PRx7OOT5+pMqrmz1miiZxnDJbMXlhrYOjRjxZqJLRFC7s9fnDV3fxwoSZksmZ\npco4V+5KY0iSptTy+vigtN110RWFALFeHOpgIBwA988V+daVAyYKJlGccGVk39YVGSQYehGaIrPV\ndpnMG4SxmBPyI7EfOD1TIG9qbHddzu/0kSSBVy5mtPFgvyxJnJwWQKa9nse5nR6GqnBmufyBdAo+\nDChKyw7GeG1Vlm87l/R2614w2t/VcgZdJ+DxI9V3Rd1vth0u7g1QFInHl6vjQ8+Ngbi1vEYtq9Po\ne+z1XF7bcpgrZ/jivcv8q+c2xeEmTblvvsj3r7TpOj71grAiVrI6ti8CtRcqFtebNrYfM5E3WKpn\n2et7xInAWD95tIoTiDiUNE0ZeCF58+aMo7fq/USLfNR6T08sSZIU4Mtpmn4B+Hcf7iXdrMNBMYCu\nEzJ/d4bF3rH8KB5z+p9ba/HqZg9dk/nT906P7GvCj7vd9ajmxAai74Y3/YxLjQGOHyNLAq8cROIB\nemI6z2TBRJEkvCim74Z8b63JjGLSGHpM3jBrFMYiX0RTFWYKFn/42g5BlKDIEroicf9ciSCORzMl\nMQM/HmUb5Dhav9W+B7DdFXMD2x2XY5N5Hl+uimycu7gF+kEqTVPyo4OuMhrWXjuwGfgRl/aGfN9p\nE0YxO10PN4j5UycnWannyBoqTx19088UxQnfuLjDTtcjo6kcrec4MpFDlSVe2+qx2XGoZHSGQYSp\nKhybFJ9Hexgwlc+gyPKotS5hqjJt2+ero/meIE74zGqNP3ljj92uwGNP5o0xovt2OkSRtofBTcG9\nP9YHqx+metke5UC4QUzb9jm30ydNIWeq/PyDs2QNFV0RnvP/eLFBayhQqP/upR1WalmmSya6IgZu\nLV10hI9NiPDTatbgD17dZavj0LYDVifzJCksVi1e3eoycCNkKWU1V2Cn590ErBh4Ef6IGNcc+p/q\nw0+cpPzqv3kVWZL4O3/2nk9lp+tXnznOL/z6d/nn373Gr3x+9eO+nLte6Qg5utNzCeOUlVoORZGY\nLQlC3Ft1arrIqekijb7Ha1s90kRQPy1do2RpFC2NH6y3R46QiNlShoEXASAjjTsw2x13fPgpWzqS\nDEmUju3qhxvtlJSLe0O8UMwNVnP6eJZ4pZ5lvmKx0XZYawwJ44SvXWhQyugsVi2ePFpFV2QkScIJ\nIi7s9rl6MGS363F2u8e9c0WOTeYJ4oScoY6BBc2hL0Kzw5iBF70jAOfjlKUrKIpEHL+5tr9XGarC\nSj1LIaOxXMu+p6zDQ3R7HKc4QTQ+/Oz1PTZaDkEU83svdrA0hZypje1zXSdiq+PSdUJhKwwirh0I\nYEWcwFQxQy1n8MB8ib4XcjDwWRwVv1VFEPsWKxZTeRMvilFlmaliBkNVuNIY8NpWD1mGx1eqtw2h\n/aToPV19mqaxJEmOJEnFUdDpR6bDQTHbj1iqfXK6Pu+ks9s99noiGffe2SIdR7Szk1QkOU8VTEpZ\njZNqnqyhMl+22O259L2QF661sUahYd5oI6GrotLuhTHzFYvZUoaXN7u0hwFHJ3LcP1/iwt6Alh2y\n0/E4MZUw9CKKGY2LewP2eh6Nvk/Z0pgrZ0bJzBoThQxXD4bUsgbFjNgwH5vIYQcJc+XMbWECc6UM\nb+z2WR75fmVZGoeCfpoVJyk/uN5m6Eecminw2EoFTXkzpfn17R6lrKD/NIc+hqpQzxnjBeutUmSJ\nak5nq+OQNVQeP1JlIm9ycYRKr+cNTFXmxHSemYLIhdBVmWtNm4yusNfzmCmJ7ICXN7tstm06TsiD\n8yV+/oFZQISU5k2NWpywOplndfLthxMPK4MrdYFanyyYPz74fAhK05RXNru0hgFHJnLviQD3Vi1U\nLAZ+OLIyCFpUEIl7Qw0ENvfYZI4nj9ZxwpjvXG5SsXS8EYly6In79xCDb6gKPS/iLzwyRckyWGva\nbLZdFFnite0ei1WLjhMyX87Q1UP2B/7YenejKlmdet7ADWMWPkEd/Pej3/z2Gs9fb/P3fvH+90SK\n/CTqgfkSXzg5yT/51hp/+Yml940d/rTqYOCjKRIlS8cJIn5wvUOSpiyOQjknCyb3zr17B7SWM9BV\nmSQVmSwzZZNT08JRslCx8KIYU1V4cL7EG7sD/ChmriIIcgNPzAL3XLHRtXSZRxbLpCnkR5/XjR2g\njCbjhaLAWc0Z48NPIaOx2/Ww/Yi5SobLjSHXmw5LVWHLW2854zDqVza6uEHElf0h9bywtSWJsKWd\necss7ULVYuhHWLpyV9ukLV0VwbBx8p5DTG/USj3Hym3mNN/279dyRHFKRldumqvN6iqSBGGS0nVD\n9vs+syWTp45U6TohEwWDrC4IbwM/pJoziKIUXVHYt12cMKKc1ZEliVe3esRxStsWcSpbHY+ypXJ8\nMk+YpFw9EHNjh0Xrw/1mGCU0Bz5zZeUTGz0Adzbz4wGvS5L0J4B9+GKapn/9A7+qGyRJ0h3RkO52\n9UbpzxISzZG39+HFyihNXQSGyrLEmaUKfVekVauKTJQkXG86XGvZpInAR8+UMuQqFoWMylTBJIhF\nd8UL4zH1ZLfncWwyx1I1y8AXlqsXrrVxRghFdXTzThdNHlkq88ypSdp2AJLEXs8bb2DunS1Szxv4\nUcJi1XrbLs7BwKdtB4RxiqrIXNofsFTL/sgsikM/Gh9kdnveTbawJ4/UOLNUoWMHFDIqti9mdcIk\nvW3AmiRJPHW0xumZIgVTxRi11FfqWWTpTXBCZ5Th8v+z995RcqZ5fe/nzW/lqq7qHNTqbmmkkTRa\njWZGk3aXXXaBXfIuORmMwQuYywVjsDHH2MvFvpwbsIFrliVdLmuiiV7AMJtn0+SsGeVW51g5vPm5\nf7zVpW51S+qWuqVuqT7n6Iyk6fCq66nneX7p+x3IRIgZKucWKuRrDvuyUd6crbBcdTg9W6JQddE1\nmYbr09VUCzw+mGnJo3YlTc7Ol7m4WOVYfypcd37Ay5NF5ssWhqrw0HCmreC2g7i+aA0EzxYbNxX8\nrPSer/DI/nAvcbyAT705T8JUsfyAjpjON58Y4OHhDhxPgBBYXsBwNvSFWGmpGOmM4wU+tifoTZk8\nOZbDC8IEihCCQi1cX7oiMVVsMNoV5x0HOtcdjIoscfwG7R53Ay9czvN//eMZvuZIDx94sP9OP86O\n8lPvPcj7f/Vpfufpi/zUHpfx3k5WqikQmlhWbY9i3WkGMfDQcBiAbEYASJYldFXmgYE0NcfjsWYS\nDFiXsHp8VMcLQlPulREBIQSfPbuI5wuihsKh3iQ122O0mbwczkaBUPL5kf1ZZooNBtIREhGN6Eh4\n5siSxMsTRQDcICCqqXQnDUw99KHzgoDXpkqtqsblfIO4qZCKaOSSJgOZCF0bVDySpsajI9mb/Cnf\nXm5nO3dEVxjOxTBUeU3VOBUNf15+04h+Kl/nYHeC9z3Qx5H+NFXb40B3AoHEUsUhqodVJ1mGmZLB\nUCbGfNlifzaGRCia4AUBXQmTi4s1dFXhcr7OiaEM3cnQPPjMXIWuRCieEYhQTlzMllmqOddsfd8L\nbCX4+dvmrzY3yfmFCuNLdUqNUL9/JQPUl47wwZODCCG4sFhrydiubhnJxgy+eGGZ2aJFR0wjGzfo\niOlr5LK15gC6qSn0pSMsVW32ZaNk4wZPjGV59lKexarFVLHBvo4YVcvj1EgHCVNt9vqHG3F3U6xg\nuRq2z+zLRjnSl7qmqs/4Uo0vnF8iX7M53JtiqWozWwrdpF+fKvHEgXvjspwwVDoTBmXLZXCDbO90\noYHl+aSjOtl4OBR+erbM2bkKhirjC9ZJY2uKvK5MrinymgPP8UMVlsWqxcPDHRzpuzJntlx10FSZ\n/dkY6YiDLEuc2t/REqToaMpoCiG4vFzl716dwwvCjXWwI8qn3logX7UxNIX7e5M4/h5xMNuj6Gpo\nILpYsRnKbk/FwNQUarbHq9NFzs5XSZkq93XH8fyAc/PVlvnoSiXP9nyeObvMdKkRSvBbHqYW+o6l\nozo9qQgPDKaYLVo89eYcXzy/zPmFKhFdIRXRWa7aYZvrPVDtvZqFssWHPvYifekIv/zBB+7KdrfV\n3N+X5GuP9fI7n7/E9z+x/4bywvcKq/dJxw/I1xwmCw00ReLx0dyWDb+7EgZVK2xt02WZN2ZKdCfN\ndf5Xsiyhb5CNl5s2GzXbp2p5dCfN1nk+WWiQr4U+YyuecyusVDksx0dTZVwvCNXsNIWZUp1z8zV0\nRSIZCcWY3pqtcKQ3yeV8jX0dobl7ytSoOf5d/17YTq4YrUucGulY02JWtq5ITM9mQ7uLquXxylSR\nqK5wtD/FYCacv/IDwbGBNIamIEkyEV2hK2EiyxKjnXGePrdIOhL6Nh7ojtObjOB4AecXqlxerjFX\nsuhOmsyUGrxjLEeh5rBYtpt+cHd525skSUNCiAkhxO/fjgfa61huOGOTjRuty4Tl+rx4ucCZ+Qq5\nmEE6onFiKL2ufDpbCh2TIWxHWt1Wlonp9CZNepImgRA8MtKxZqjs0lKNCwtVMjGdI30JDE3mcG+y\ndXHuiOqULZflqoOpK+TiOkPZ0IBqX3bj7PJ9PQlSUY24rl5XzvRz5xaZL1lMFuqhN1EmgusH6IqM\neQ/JoMpXZbaLdYdXpkroisxwNsr5pvY9wKGeJNPFBvmqQ6kRVsty8bBFabUk6mZYmQULgrD6VLU9\nZooWA5kIh3sTpEyV0VwM2ws42B1jruKEZetmZrBmezx/ucBCOfSBMjSFxbLFQtnC8QIkSaIjpjHa\nFW9lHNvsHId7kxzu3d6vWbE8Li7WUSTQFZmjAymmiw1miqESY9xQW5ni6UIDibDPfbAjwmMjOT57\ndoHXZ8p4geCrjvRgqGFmUpElYoZKueGSjGhoSqjupMph+2Xd8RjIRO+J6m/D8fnQx16ganl87AdP\nkYre/f9mgJ987wH+/vVZfvOzF/g37z98px/nplhtRbAdDGdjCEHr/XB5ud4yJ96s4fZkvs75hSq5\nuMGxgRSDHVFUWeKLF5ZpOD7zZYuvONh1w9ajFQXO5arDufkqixWbpaodGhsHouXJ8tmzC+zPxRnr\niq95v44v1Ti/UCVmqDzQnyQTM8KKgR+2eiuazKGeBKWGhyxDMqrx7kPdzJUsPBEGgecWKiRNldHO\nOFZz3jUX10nv4la3243nB7w4UaTuhCpvEP58G47fCn4s1+eN6TIANcdlsCPGQMbkixeWGV8KBRkG\nM1GO9qfQ1U4UWaIzYZBLGKGvlKm2TEeFCG1VzsxX6EqYPDaSJaKr9GcivHC50PR7s/GDgP5MFL/p\n/9YZN5Blifv3eEfWZkK3vwIeBJAk6c+FEB/c2Ufau3h+wDOX8rhesEbWeKlqU3d8OhMGEmF2f6O+\n0dUZ/42CjeFcjKlig8FMdJ2axopsYaHm8NpU6KsjSWG7VURXGM/XWSzbzJQsHhxK05+JrsnSzZUs\nZIk11SblKsnOuZLFVKFObzpCd8Kg5vgkTZXeZISFss2BrgSyLFNzfE4MpdFVZcMKyL3CXNnC9QLc\n5uChLIcBSqT5OmeiOpflGhFdxWgeEvEtDlNC2DfdcH00RaY7YfKZswvYXsBssUEuYVB3PI70hatr\n/gkAACAASURBVL3ln35rnhcuF1FkiW9/eJC+dITlqoPrBWSiBkMdQRiMBaLlTdCbNnnXfV137ezC\nvUB/JoKqQDKik4rqGJra8tqRJNZk8dJRnYrlsVS16U1F8IXg7FyVfNXh43MzDOeiHOwOD76TQxkK\nzYRKOqLRcEMlt0tLNZ4fzzNVaDDaGeNdh7ru6ouO6wf82B++yEuTRf7rdz245QTGXmasK8E3va2f\n3//SOD/45P41Z8heYDIfZtnTUY0Tg5ltmWNQZGlNO/PB7jgXl2pkY/qmW6cmC3X8QDBftjjoxVut\n5qYm03DCOZ/NPmtUV4l2qCzXwsRXVFeRZQlNCv1xFqs2VcujUHM4v1Dl5L4r7UxzZYt83eH8QoXe\ntEEmFs4gvf1Ajs+fW2I4F162z8xVyMYNYkbY1dCdNOlNmXzh/BLn5itMFxp85eEuLDdsmZ3M13nn\nwfXtsXcjNdtDkaXrvvalhttKZMYNlaSpE9GVNXLRrh9QbIQ2BFXLw/NrvHi5gKnJBCIgoqlkEzqS\nJK3pCtqoZU9RYKEcdukYqsKrUyW+7ngfcUNtKsBV6YiGz6DIEucXqqiyjJACvvpoz66Wsd4Mm7lp\nrV6ZIzv1IHcDvhB4zXL36iHCXNwgqtfRVZm3DaWvKQPYEdN5eH8HQSA27AW+3kD6cC6sLGRjBkGz\nHzRMZIXShI4X0NeM3pMRjZihUKg5XFyqNs3GwmHnBwZYd3jVHY/xpTpvzpZIRXRKDYfLyyp126cn\nZfKe+7s4OpBECMHpmbDP2Qvg6DWe9V6hJ2kyXw6FAgY7ovSkItiuT6nh8sLlPKOdcZ4YyyEh4QcC\nNwhuSiLSUJVWoA1gqgqvTJaQCUvkXQmzVb5uNIcWAyFouD6T+TqFuoOpyShSKLCQjupcWq5yf28K\ntanwNbFcXxP8VG0PQ5VbWaQ2uxtNkfm6B/p48XKBXMKgL2UiSRKPjmaRuBL8BIFgrhT6RuxvZq8r\nlsdYd5wvX1pGAv7xjTnGOuMIJAp1l+OD4UyDrsg4ZYtC3SGqq7jNvVCWpJZi5wp+EKoYxQ11z7fD\neH7AT//ZK3zqrQV+6ZuP8r5j21y22wP8xHsO8NevzPDrnz7Ph7/x6J1+nC0xV7YQAgo1t2kgvf3t\nPOmozoNDWwv+BzNRzi1UyK0aOgc4PpAmX3daam5b4YH+FGXLbUmTS5JEb9rEcn2aYnTrqrT7slGe\nH88T01UuLdUZzoZBnSKH1hayLHN2vspS1SFfd5pmqBIXl6qYqsJgNsqFxRpCQMMJWlUNgIoVVoz3\n+h6wERPLdequR1xXeWuugiyHkv/XOuNTEY1kRKPmeAznYutaGgFOz5SJaiq259OTMnj2UoGkqbIv\nm+LRkSydCXPd6yeEaIpKqGtEijxfMNoVR5JhvinAdXa+wqMjWfbnYuzPxXj63CK2GxDRFPwg9GdK\nmForgbuX2cy7XFzj9zeFJEnDwDPAm4AjhPiqW/2aV+N4AS9cLmB5PscH0retD9lQFY71p1iqOq15\nHgij7sfHNjf3crPtIb2pSGuOw/ODliHVuYUqC2WbqB4Oqz88nKE/E0oXvj6dp9xwWaraJE2tNYx5\nNW/NVchXHfI1F1NTyK1SKCtbbsu9OQgE82Wbqu2t8f+5V0lHdd558IrEi6GCIkm81BwcPSeqa9Rv\nIoQKbadnSyRNjRNDmZtSVDvUm2jO+kiULQ9Dk1uvx1cc7MTUFJKmSkdU54XLBQB6UiZH+1M8N56n\nVHd5aF8H+3Ixzi9UcD2BL64sjMvLNc7NV9FVmVMjHfeMhPleZyATXVe9u9qfZalmM1NsENEUKrbP\nYDacA/iKg508fXaRhapN3Qma+4RoXZh6UyaLVZuYoTJbtHh0pIOvONTJbNGiM2GumVtbMf2rWl5r\n3e1VLNfnX/zhS3zizXl+5mvu47tP7bvTj3RH2JeN8R0PD/Lfnpngex/dd80k3W5kMBOl4VTIRPUN\nJafvFIMd0Q19DVVFvun2Y1mW1lRgg0Bwbr6KEBAzFB4e7ljXldKbivD4WI581SEd0SjWHV6eLDKZ\nr7cu6A03TJ4KAQLBxaUaU/mwpfZof5LHR8Mh/YeGMwRCMF+yuLhU47nxwp7dA4QQvDJVIl8Lu15W\nv1aFmsPZ+TAR7AuBIklhW7rlXTP4URWZR/Zf38vFF6GYRdRQSJgqh3oSzJctNEVmfy6+4X3h9eky\n82WLhKlyapW4xGAmiusHDGejzFdsGo5P5qrq/Ml9YbtkZ8JAbpqsJiPqnp/3gc0FP8clSSoTVoAi\nzd/T/LMQQtxM499TQojvuYnP2xTFukPNDi/ncyXrhsFP1fawXH/DSHurdDVVs4QIezVNTb7tWQ1V\nkVuXnFKzjNpoZnJXl5jTUY1yw2WwI8JAJooiry2VrmA2L7djnTH0pqR2Lq4jYM28kCxLnNjD6h+3\nA12VieoKdcdv+SysZqbUIAhCT6uq7V0zGM7XHBRZ2vD/Z6I6qioxW7J458HONbNjUUPl3Ye6gHBG\nYaUVz9QUbM/ngf4UbhA6hEuSRNJUWais9WQp1sM15XgBDcdvBz93CUEgUCU59HtorgFdCZ3da7bP\nAwMpLi/X6W5WjRRZ4sRQmuWaQ386wvhyeOFRFAlNlRnMxBjMrJ8n9ANBtZk8KV3lXbaXWK7a/MjH\nXuS5y3k+/I1H+L7Hhu/0I91Rfuq9B/mbV2b48MdP8//900f2TDa/J2VueO7tNibzdS4t1ehJmWvM\nzm8GIQSWG2BqMsmIRqnukosb15RxfttAmrrrE9UUzi5U8HxBzFBpuD5jTTXZyXydhKkR1dXWnUGW\nIW5q65K/fekIZ+fDWaOr/QtvBc8P8AJxW1TZ7KbkM4TmsauDH12VW2frvmwU1ws98XpuoSW0VHcZ\nzkZpuAG5uEGpHgof9KYjPDrScc1E6coeW7G8Nf5xsiwx1hWuo/2dcawNqp4r7ZIrjHXFKdQcPn9u\niaihcHwgvWctL24Y/AghdmIVvUuSpKeBvxBC/Mp2f/FMTCcZ0bBcn7709Rdb3fF49tIyQQD7O2Mt\n+cey5fLWbIWornCkL7nljXzFZ2dFy/9msZqtSamI1mpHc7ygVVIezsUQQjCRr4dmg02p7BXu60kw\nma/Tk4qs+fti3SET1elLRzBVGVWRw55TyyVpaoim55Aqh8OMuYSOjMTLk2HVwgsEDw2vz1JYro8q\nSxuan7YJWwUe2d+B5QWtrPtsqcHl5Tq9qVASdGV4PHGN7MpMscHpmTAH8eC+zLrgvuH6aLLMYCbK\nUtUhojWYLjboSZr0ZyLMlizqjs++bJRT+7M0XJ/pQp1f++Q0PSmDb3xbf8usLqqrjHauPRBHOmP4\nQpAw1Lt6juNeIggEzzarMb1pE8f1W3L3Y11xorrC/s44k4UGtuvz6lSRfdkoZ+YqaHLoIzScjZKN\nGcSN0Bh1ohYO4C7XHAYykVZCRm2KeixUrA3FVvxAMFtqEN/F6+uVySI/8rEXWKo5/JfvOME3HO+7\n0490x8nGDX7yPQf58MdP88k3F3jP/d13+pHuKsaXazhewMRyndHOjbP8V1Nv+nQpssRy1W6K1+hr\n7icnhzLUXZ/YdapesiwRN9TQA0iRkSTBQsVmOBtlKt/A9QTDuWhrML9qu7w4keeBgcy66jLceA+4\nHktVG9cP6Emaa+5ltufz7KU8thtwqDex4zOqhirTnTRZqtnrOl1ihsoj+7ObTqqfX6iwVHUY7Yyv\nqZK7fsBy1SEQonXmH+lPkmqKy2RiHZiact328/t6Ekzka+iqzPnFKgOZSNO82mUiX0eRQrW+3pRJ\nEAgKdQfbC1odPFebmU4VGliuj+X6FOvOnp39uRO1q1ngIGADfy1J0ieFEK+u/E9Jkn4Y+GGAoaGh\nm/oG2ibKhys4XmjABeHFfYXxpRrl5gBaXzqypda5Fe8LgHzd2fyDb8CZuQqLzezC42MqUV3l0qqS\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x1akihZqLock8OZbb9LD0yvoRAhRZ5tRIFs8PrrsvCSFYrNoYqnJb1pkQgtlSOOvx2nSJ\n16ZLvDpVag05H+1P8kvffJRvelv/rpHWvtvoSZn86Yce43t/5xm+57ef4Re/6SjfenLgrpDM3QpC\nXDEKXT2rsx0oG7Qx7yRe0xcGruwDXQmTR0YUJLhuotIPBEtVm4Sp3tVKgHFD5W27fB7q/t4kvSmT\nuBm+Do2moNTK/dEPBM+O53G9gFzCWPfvCYJwP48bu8eaYDPsnSdtswZNuXFLyqHeJONLtaY3x7UD\nn7rjsVC26UwYrcW7Wn3uRvMpS1W75bp8f19yT0pR7jZW//xXWpR2guFsjIWyRaXocnmpRtJU8QKB\n64sNq45t7k5a5oW+QIjNzdVVbY8gEHTEdKKGQm/S3FSbxGpBhUf2d2x7NddyfZ4fL/D0+UVenSxx\nerbcUkeSpTAb+86DnbxtKM27D3VtKOPfZvvpT0f4s3/+GD/+Ry/xM//9VT5zZoGfe/9hBjK7uzVq\nOwnElbmSaym87hV0VeZwX5Klis3wqkTK6grHXMnCCwL6r1Kfe3O2zFzJQlUknhjL3VIFrM2tIYCa\n7SMIfZQO9yZZrNgtH6VAiNbs50Zr9nTztVQUiSdGc7uytW8jNhX8SJKkAP+7EOJf7fDz7DkuLdW4\nuFilO2nuOn30VES7rgpLEAgqlserU0Xspm7/yvzQQCaCGwTIkkTfDS7BawKlPb6h7xb6UhEcL0BA\nq6d7JzA1hf25OI4XNouPL9WoNk3PZJkNZ5MqloumyK3WoIWKxRvTZaK6wsl9mbumLH4vcXQgxUyx\nQWfc2HSf94uXCzheQNRQeHBVBvtGilMre4QQ4aH7+nQZy/M5PpC+KXNkIQRvzVX4/LklPndukWcv\n5bG9AE2RuL83yfuP9XB/b5LDzV97KTt5t5GNG/zBD57iI5+9wK996hyfeHOBD5zo59sfHuT4QPqu\nnjGAsDpzfCDNYtW+K4Lu/nTkmv+O8aUap2fK6KpMEMBQ9kqQazf3AD8Q+IHg6i5Tzw944XKBuuNz\npD95wxnZNiFV20OVpS217Z5fqHJpscr5xSr7O2M8NpJbc2/UFJkHBtIs12wGN0hUrCTHg0AQ7CH5\n+E2dAkIIX5Kkk5IkSWKvi+NvM9OFBkKEGY5DPYk9dfF7bbrEYsVmfLm2JnMDocDA6CZN0XpTJrYX\nEAixznW5zc0hy9JtM3rsT0da2UhJgupC7ZofuzIjpCgSp/Z3ENVV5koWfjOQLlveTV1g29xZkqZG\nsmdrFZiNDoJi3eHFiQJBAA8Mpja8tIx0hkPFpqYgy+FwLYR76FbWju35/Js/f42nzy+x2DSZHuuK\n812nhnjHgU4e2d/RDnR2IYos8WPvGuObT/Tza586x1++NM0fPzdJNqZzaqSDQz1JDnYnGMhE6EtH\nyES1u6o97upZnbuRhYrF8+N5Jgp1RnPrz7H7e5NM5OtkmjOGV1O2vJZYwFzJagc/m2BlVEGWw7GI\nrVTUa46P5Qb4gdhwH+5MGNccfTjcE76W6Wu8lruVrZwMLwF/LUnSnwGt25EQ4i+2/an2EIMdES4u\n1ehOmHsq8IErSiR9KZORzthNz5VIktRSMmuz91gdaImmV5Uqb6xIV240+4B9Qa0puNGXjlCou8SN\n2zPD0WZ3cHJfhqWKvWbfCFvhmr+3PDaaHdYUuaUE5/oByYiG5fr0baB4eT0MVWF8ucZjI1mePJDj\n7Qdy95RU616nLx3hP33gAf71+w7zyTfn+dzZRV6cKPJ3r82t+ThDlelNhSaPXQmT997fzdcf77tD\nT91mM1Qtj3RURwD9mQiDHWvflxFd4b6eawsLpCIamZhG1fbbbfSbpNI0qw+CsKK+2eBnrCuOJksk\nTBVTU7a8D9/otdytbCX46QCWgXev+jsB3NPBz9WDw3sFIQSaKjFZsHloX+a2VRna3H5cP+DN2dDY\n73Bv8rr91ZIkXbcHf6QzhhcEmJpCLh5mh3Jxoy2LfQ+yWr3u3HyFsuUx2hmjLx0hEGJTsxyaIt+S\nYfFf/OgTN/25bXYHqYjGBx4c4AMPDgBhJfDCYmg+OVO0mC01mClZLJQtXp4sbrojoc2d6qd/rAAA\nIABJREFUI26oFOoOqYjGiaHMlit3iixd19+uzXqGslEsN0BTJbq2IHOuyBK96Qgly8VQlV2rSrfd\nbDr4EUL8wE4+SJvbS6nhUml4dMaNlrpHm7uT6UKDhXLYFpSOWGt6r7eKqSkt+cs2bSDcSy4vh94e\n47K069WN2uxuYobKAwPp9j6zhwlb2sLkmO1d35KjzfZgqArHBm5u7vxyvtbyg8zFdbrusBz87WDT\nfVqSJB2UJOmTkiS93vzzA5Ik/fzOPVqbnSSiKy1VjnSkPaNxN5OKaMhyKGCQjLQPoTbbS0RTMLSV\nveTeyBq2adPm2qSj4T4Q0ZUtWWy0uTOs3AEVRWpJXt/tSJvVL5Ak6bPAvwJ+Uwhxovl3rwshju7U\nw+VyOTE8PLxTX77NLSAE+EKg3gZ1nvHxcdrr4O7jZtZQey3cm3iBQJGklgT3vbgOvEAgS6FvW5uQ\ne3EdtFlPex3cHlbObEWW2I270AsvvCCEEJsq6mwlxIsKIZ69qnfT29KTbZHh4WGef/75nfwWba5B\nw/E5PVtClWWO9CXXiDn4geAL55dwvIDupHnTpdbN8tBDD7XXwU3i+gFvzJQJhOD+3uSuUWMRQvCF\n88tYrk82rnNiaHNmf+21cPdTqrucma+QMFUO9SR4ZarEUsXG0GSeGM0hy9JtXQdBIDg9W8Zy/Tsm\nlT2Zr3NmroIkwcP7O+6Zvvwb0d4P2kB7HdwsfiA4PVPG9nzu70vesD3xixeWqNs+6ajGQ8O7byZL\nkqQXN/uxW5EnW5IkaZSmwqkkSd8CzG7x2drsEaYKdQo1l8WKzUJTRnYFPxAtaeT2vNDuZq5ksVSx\nyVcdZoqNO/04LQIBjh+unfYaarOaS8s1yg2X6UKDcsNrucg7TTn9281SzWauZFGsX5ltut2svEeE\nCE1c27RZoe0+0uZmWarazJfDvW0if/29TQjRMsO+G87sraSwfgz4KHBIkqRp4BLw3df7BEmS+oCP\nA/cDcSGEJ0nSrwAPAS8KIX7i5h67zU6TjupM5OvIsrROvlhXZY70pViu2Tf09SlbLvoqQ8w2t5dU\nVEORJQSiNYB6J7FcH9sLSEU0jvanWKzY95TDe5vr43gBWrMN0tQUoobCkf4kk/k6nXHjjtgJJE0N\nTZXx/OCOeVjtz8UIhMBQlVv2PLG90NOjLUu/t/nc2UV+/dPneXmiiCSF0vPf99gwX32k+67yRWqz\ncyRNDVWR8ANBx1X3AyEE5YZH1FDQFBlJkjg2kGK+bN0VBr1bUXu7CLxHkqQYIAshKpv4tDzwlcBf\nAkiS9CBhEPR2SZJ+Q5Kkh4UQz93Mg+8W6o7HTNEiF9dJ74LL5XbRmTB48kAOWZI2lEbuafouXI/L\nyzXOzVdRFImDXXFsL6A/E2kPQN5GkqbGkwdyCEFL4GK7cf2AyXydmKFe1yvKcn2+fHEZzxeMdcUZ\nzsVaF7n5skXd8RnMRPacX1abW2O21MByA2KGwqffWiBmqAxnYxzuTaLI4f5zpG/zrbXbvSebmsIT\no1n8ZvBxJ9AUmUM9yVv+OqvfgyOdsS1ZHMwUG7h+wGAminwbZj3bbIwQgl/5xDl+9ZPnGOyI8E8e\n34cXCD711gIf+tgLvP1Ajv/zW4/ftG9fm3uHiK7w5FgOXwjyNYdLSzWGOqIossQbM2XmShZRXeHR\nkSyyLJGLG+Q2YdBbsz3myhadCWPXtuhuOviRJCkL/ALwJCAkSfo88GEhxPK1PkcIYQHWqizEo8BT\nzd9/AngM2NPBz6tTJaqWx2S+zjsPdt5Vh8JWDnohBHXHJ6IprZ/Biolqw/F5frxAzFApW15bCvc2\ncz1fn+3g3Hy11VIXGbm2T4Dl+nh+2KJRta+MC5bqLq9NlYAw879imBYEgobrE9WVdibzLsJyfWRJ\nQldlCjWHN6ZDD6rJfI3lmts0tYyg3ORe+vp0mXLDZTJf5x0HO2/666xGVeQttUlsN54f4PjBLUsG\n226w4XvwRixWbE7PhK+TH4i2L9wd5LeevsivfvIc33pygF/8pqOtrop/+/7D/NFzk/zS357mG3/9\nC/z+P31kT5pPttlZ/ECskR9XFZnKqn3Y8wMOdCda97e64+MLgbwFiYNXJovUHZ+pQmPXegBuZSf9\nY+BzwAebf/5u4E+A92zha6SBi83fl4AjW/jcXcmKUtVuDHqEENe8NFquj+X6rcxoEAguLtWQJNif\njSHLElXb49x8hYSp0Z+O8PJkEYC3DaaJ6GsDo5Wh5I64zoPN4fWRzrBVQ1Uk5koWQQC78Md013G9\n130nUJXwe9Wa62W0M06+5vD0+SWShso7DnaSjRukozrDuRg122Ok84oxsLQqNltZH0IInhvPU7E8\netPmljL/bXYHV6/DQs1hsWLx96/PYfsBT4x0ULJ8SnWXzoRBzFAxNAUh4GD3zV+uV2J9SWJXKhJt\nFdcPeOZiHsv1WxVT2Nr7vFBziOgKCVNFICg1XB4cupKE8gPBK1NFarbHkb5Uq73P8QIuLdWw3CuB\n0nYEk21ujpcni/zy/zzD1x7r5Zc/+MCae4eqyHzvo/s4OZTh+3/vWb7lI1/kj37oUY72t/fOvcZO\nneF+IHjm4jJ1x2c4F2WsK9H6+4rtEdevJK8P9SS4nK/TmTDQFBnXD3hlsojtBRztT61rm720VOXF\niSL39SRaz76bt4qtBD8dQohfXPXn/02SpG/a4vcrASu1+yRQvPoDJEn6YeCHAYaGhrb45W8/xwbC\nuYVMVN8VAVCh5mBqCtPFBuNLNXpS5rrNz3J9vnRxGd8X7O+MMdoZZ6oQfjyAocoMZKKcX6iyXHVY\nrjo0XJ9aM1M4X7ZaB3Dr+9ZDg6xi878AUV1tGdX1p6OUG+4NW+Xa3DyWG1bYvCDgbYPpG7b8FOsO\nhqqsC2S3ylhnnJih8vJEgULN5WWryEyxwaWFGvm6je0FvO9YL6mIxljX+ktt0tR4cF+GhuvT22zV\n8APRyjwV6+4tPV+b289UIVQnS0c1TgxmOLdQ4fRMmbNzZS4XGiCg2nB5x8EuUlGNI/1JkqZKvhYG\nQrcyI3isP81Cxdo1e/Kt0mgmqiDcZ4eCKC9NFinWHUZyMZIRjXRUv2ZQcn6hyvhSDVWRGOmMISGR\njugsVh06mi0sxbpDvmlyOF1otIKf8wtXqrr7slFihkpvew+/I7h+wL/805fpThj8pw8eu+bavr8v\nyV/86ON8+29+mX/yu8/ypx96jNF2pe624HgBFcu9pb1nfKnGhcUq2bix7V0ytudTd1b2kvBcDQLB\nW3MVEAJNlRlp3u0yMZ3MqhnHfM1pncUzxca64Ocf3pinanlMLNf4obePUKi7ZOO7dxRkK/0wn5Yk\n6TskSZKbv74N+Nstfr8vEc4AQVgx+vLVHyCE+KgQ4iEhxEOdnbuzXLYaQ1UYyETviPzp1VxcrPLC\n5QJfvrjcCmRmSw1KdYcguKIIY3sBvi9YrNi8cLnAhYUKixWrFdystLslm2ZXmirTn46gqTKSBBeX\nqnzmzAKF2pVA577uBKmoxn3X6EtPRTQGO6I73oJ1L5OvOa3WsqsV+q7m0lKN58fDtbKipnUjpgp1\n/ufrs3zsy+N8+q35luKfLEskTbV1+TI1hb50hEAERHWFuKHe8Ht0xHT605HWgaEqMge646SiGgdu\noQrQ5s4wV7IQAgo1l+WazV+9NMNnzi5yerZMse6iqxIDmQhCCA52J+hNRYgZ4R5xq+IoejN5s9N7\nsu35m37vrBAEgpcmCnz6rYVNqy8mTY2hbJR0VGOkM47l+RRqDkLAZ84u8tJEkVemiggR7umr29kK\nNYfLyzWEEHi+QFdkFFmiZntMF+vMlsJnSEY0YoaKLEN38kpPv9k0r5Vl6E1H6EtH2i2od4g/fnaC\nC4s1PvyNR284RzGQifIHP/gIkgTf9zvPkl91VrfZGYIg7FZ4aaLI6zOlm/46M6UGQsBSxcb2tra/\nlC2X6WIDf9V9TwhBxXIJAkFUVxnOxUhHNcY647h+wFypQdV2SZgapqYwVWjwidPz/P1rs612dAiN\nayO6giJLdCXWz/2sGNvGTY24Ge7lt9qmu5Ns5cn+OfBTwMeaf5aBmiRJPwUIIcS6W68kSRrw98Bx\n4B+AnyOcAXoaeFkI8eytPPxeoGK5eL5YE0FvFzXb483ZMoaqcKQv2Yro/UCQjKhULA/LCXhuvEAy\novHI/lCXPRUJ29gu52uUyx5vTBdxfUHCUPn64310Nhf2SGccwZXM+6HuOLNli8WKTQDMliwyMR3X\nD1iq2miKTG4XR/p3O9m4TsJU8QJxw+zsSqBbtlymC3VqzbVzsDvB+YUqdcdjtCtGLm7y/HiexaYg\nwWfPL1JuuMR0jYbj85X3d+N4Ac9eyoOAmKFgagqZqM6/eNcY8xUbU1PWXKg2y75sjH3Z2I0/sM2u\nIQgEr06Hh7/tBmiKzMWlKucXykws1wkkeHgoDZKMIsl0J40Nq4HbgecHvDlbwQsCDm+zx1XV9nju\nUh4/EBwbSNGdNPH8ANsLrht0NVyf5WaFZabYoG+VatJy1SaiK60LgxCCFyfCCs+j+zs42J1gttRg\noWwR01Us329dguu2zyfeXODcfIWOuMbh3hTZmM6ZuUprxupwb4KeVIR0VOeLF5ZD/6KZMrl42Nby\n2Gh2XbvNSGe8eSmSie+CBN+9Ss32+M+fOMep/R185eGuTX3OSGec3/3+h/mWj3yJH/tvL/IHP/hI\nW0xmB/GFaFVoa/bNS0EPdUS5sFgjF9e3NHdtuT5PvTFHzfY5NpBqeeetCBfoqkwmqpOKaLxtMM1s\nyeKVqQKeH1as+nNREqbCU2/MM1mo050yUGWJ/kykVRV6YizHW7NlLizWUGSJdFSnWHc4v1DleF+K\nh/Z10JfeG5Xhrai9bXlyTgjhsn4m6Jmtfp29Sqnh8vx4HiHgvp4EgzeQhd4qE/l6MzBx11wilms2\n5YaH2RQfaNg+hbqN7fpoiowsS4x1x1mo2rx4uUDN9pkrW6QiOm/MlhnrTmBqCkIILi/XCIKwgmSq\nSnjA+wHpqN66YM+VLBbKYaVhylTbJfY7hKEqHB9MtxSyrsdYV5xC3WG+7PHseB5DVUhFNBwvYLFq\nc3a+wpuzZQ71JPn8+SU8P2C+YiECKNU9uuImji9oOD5+IBAirNaULLe18WcGUxwbaItb3Cs0HJ/l\nms0zF/PMlSzGl2tEdYUgEJQaHo4fkIzoTJdsRnIxCg2XyYLFA4M78zxzZYv5sgXAVKGxrUFW1fJa\n2dVSwyUb03nmUp6G4zOci13ze0V1hWxcp1h36UoaNByfiK60WtMUWeLRkSwRXWGyUOczZxaa3j4B\nX3Wkm9MzZYSAmKHyrvu6WChbzJdt0lGNfzw9R93xmbhcJ6KqTOXrGKqCoSoMdkRbrcqmppCL6yyU\nbQxVDrsCmnesjao6nRtkedvcXv7kuUmWaw4f/Zr7tlR5e2AgzX/85mP89J+9wn/8u7f4d19//w4+\n5b3NiirlYmW9BcjVIgPXYyATvSn7h6rlMb5cRwi4uFhrBT8ryevXp0uMdsWYL8vMly1KDZc358qM\n5GLETZUjfUlenylRtt3W3pavOzz1xhwV22N/Lsah3iRThbBafGGxxsl9OucXqhTrLkVcHhvN7upq\nz2q29JSSJGWAA0ArtBNCfG67H2ovI4TA9QW6KmO7Piv+Y5brU3c8DFW54cDoQsXijely2DJkqthe\nwKGexJpF5foBpYZLoe7QlTRbJcuj/SleuFyg4IUtUKmIxpm5Mroi81cvT5GNmbz9YA5DVTg1nGEw\nE+GN6VLzAiyTjujIzc217vg4XtjaNF+2cLyAoY4oxwfSa2Z+VrxkAiFIt70j7hhzJYvXp0uoisSp\n/dkNZ3mCQLBUtYkZKvs6YlxYrDJdaJAwVDKxFAOZCHPlBpWGS6nusly1WW72+ppqOLvVkzQ4MZRh\nrDvRmis60B3HcgN0Reb8QgU3EETa3k73DBPLdV64nKdqewgENcej0nAp1m0atoeuq3TGDUZycY4O\nplAkmartcnxw54axk5FV+1J0e/elroRBb9rE9QVDHVFsL2CpYuMHYs3c49VIksSJoQyW6/PMpTxn\n56oc6k20MsZ1x2vN+JyZq1BquDRsj2LdoSMe7vHLVZulqk3CVLm/N0lXs+o02hnnvKiSNFVihkpE\nVxjpjDFbbNCVWFuRP9qXYjnl8NZsmafPLe1Icq7N9uD5Ab/7hUuc3Jfh5L6OLX/+t5wc4PXpEr/7\nhUucGungq4/0bOvzLVQs8jWHkVx8x+wU9gobWYCstMNVLY+upEEgQqGsFSn/6+H6Qcvf60bETZWR\nXJyq7TK2qlX8YHec33r6IhcWKxTqDk+M5VAVCcv1cb2AiuXx0HAHlhcmMg92xXFzgnff18mz4wWm\nCw1qtkcgBMWagyKHvkDZZjdTWP1xieoKRvP1XzGk3s3+jluRuv5nwE8AA8DLhLLVXwLevTOPtvcI\nAsELEwVKdZf9nTFGcjH2d8Zw/QDPD/jC+SU8X/D2Azniq3p2CzWHxapNXzpC3FCZLVr4gWCm2EBC\nIm6qXFysrREuOL9QpWp5xA2VI32JNRfdsc4456mQimi4vmCsM8GLEwUm8z4J0+Jgd5z5ik254TLa\nFScd0zk2kGIwE+VQb5Ka7TJb8riwUMX1BSDIxgyminUUWVqX1UiaGo+PZRGCXb3Y73ZWRCc8X1Cx\n3Q2Dn2fH83zx/BKqIvOdjwzScMIByL50hMdHc/iBYDQX5eWJApeX63TEdGQEQRCgKRpxU2V/NsaD\n+zpam/wrk0UWKzYjneF6v7Rcw/E8JvL1tkrbXYwQAscPKNVdPn1mnufG8yxVHN4+1sGTox2cna8w\nX7KQJND9AEyN/o4o772/m6Spoyo3rlDeyrMZqrxj+5IsS2vW9kS+zvOXC9iez75cBMv1KVsu2ZjR\nuuC4fsDl5ToRXcFUZdxmYqlYdxnrijORr1NueJyZqzSV6iQOdSc4M18mZqg8eynP1x7t5aWJPEJI\nTBXq9KZMsk3z1/t6EtRsD0WWGM6F2eP5skW+5lKoFxnriuP5ATFdoe4GpKMadvMZFip2O/jZpfzD\nG/NMFRr8/NfefNXm595/mOcv5/nZP3+V4wPpbREemszX+YW/eYNPvbUAhDMfP/f+w3zbQztUyt2j\nuEFAtSnec3a+QjoSBg0dMX1N2+vVXF6ucXo2TFwfH0yv89dZrNgU6g6DmWi4p2gK77m/i5rt05Uw\nWK7aoUS1LLFYsTFVlVLD5eRQBlNXmMzXabg+syWLyeU6yzUbSZLIRHWOD6WJ6ir39SSI6QqBCFt2\nJ/M18nWXh/d1tBLgY11x+tImhZrL584tokhSKKcvwbH+FF271G9qK5WfnwAeBr4shHiXJEmHgP+w\nM4+1OyjWQ+W0zR6cKxcBCBfmaGe81QL27KU808UGSxWHQIQqa7lYaBj18mQx7IdH8IETA/RnIhQb\nLt0pE8vxmS9bRI1QalAClqpOqyypqzKmpnJxsUpEU+hNR0hFtVaGqO541B2f3pTJbLEeOpULwWyx\nQcVyeG2qyFzJomy56KrCcGcMU5FJxzRmCxYBAlNVmC2HZld+AAI4Nx963I52xpGksC0jdouqYfcq\nYVuQS9xUb+kyuC8bpe74LFdtLi7UkCWptWHOlhrNgWiLcsMjQLBQtpgr1hlfqlNuOCQMBcsNuLRc\nY6rQYKZksViq4REOSbu+4GBPgrrrtwYxXT9gtthgutjg7FyZd9zXRRAIDE1pq7TdxUwV6jz1xhyX\nmipiri947lIez/co1BqM5BLMFutULA9FlulOKvSlQxGCbMzYltkDIQRL1VDCeWUeZalqU7M9lio2\nhbq7bTLpFcvl7HyFsc44qWa1UwjBXNlCV2TmSivqRxo1K+C58Ty2G5CKahzuTRI3VD53dpEzcxUy\nUZ1jA0lycQNfBAznYpiaQkdMR1dkarZHd9JsZYoNNc1EoUFX3OSV6RKXluoEgWBfLoqhysyVLPzA\n5+9fm+P0bIVMTGcgEwpHWG4Y3BRqNh/93AK6LKMoEqf2d9CbNulNm5TqLhKCt+bKjHbG26I0u4zf\n+8Il9mXDpMHNoqsyv/odJ/i6X/s8P/knL/Oxf3bqliTLX5sq8f2/9yy2F/C/vucA+3Mx/ujZCX7m\nv79Kqe7yQ+8YuemvfbdQqIUquT1Jk9GuOEtVm4GOCNOFBrIkkTCvXL8dL6Bme6SjGpIkcWGxylP/\nP3tvHmzned/3fd79fc++3X0HLgACBEASJEVSoiRribxEUiw5buuksWPXmdE008nUTpeZtm46nXo6\nk2mnmbRNPLbb2Mm4TmQrjjWyHClaqNUiCRIgCRA77r6ce/Zz3vPu79s/nnMPAQIgFgEQQeL7Dy84\n997znnve93me3+/3XU5tUbc9Hpsp0ur7VxU/Xhjx2lqLes/n7FaXnz08TqsfsNF2GM2a1GyP11bb\n+FHMTNFiviyakpMFizBOUCSJjKGiShLbHZc//KslNpoOo3mTx6YLjAwouZ1+gBNFZA2NOEnY7vpU\nOy6K3KSSNYZadlNV+MHFTbbaLllTpZjSSRsqLSd4TxQ/bpIkriRJSJJkJElyRpKkA/fsyn7CuFyz\nuVjtoSgSz+0p31IBZGoKs+UUta43tAvcxeJohks7PcZzpnBCAjaaLs/sKeEGETs9j4yhcKnW44nZ\n4jAY6uRKi5eWG2x3PAxVhD0KS9KEcsZgppjizY02L5yrocgSv/DkFLMl8drxwC5YlWGz5bBSdzg4\nqXJypckb6x1+dLlO1tTo+xGFlIYTxHT7Ia04oeMGOEFEWldRZYkojrlQdej7EW4QstzokzGEELbt\nhGy1XdKGyrN7Sg/dgG4TJweLWNpQeW5v+Y5/T0oXvN3vna/R80IuVHtUMgYXql1+97uX6HsRT80V\nWW3YOEHMV9/Y4vWNLj034OxWh7YTEkUx02WLN7c6tPsBOUsjoyvoqoIbREiALEmc3+6RH1jsdr2Q\nN7c6FCydrbbLbClF2w2YKz/sJL9X8cpykz99ZZ2drksQRoDIBQtiaDsOm22POAE/SqhYovD5/BNT\nHJ4u3DXR9aWazeUdG1mGZ/eUiRM4sSJcz9ZaDjPFFDs3cD3c6XpIEreUVh6GMf/Xty6w2nAYyxn8\nxqcOkDFUlut9LlR7ABycyLJ3NEMcJzw2k+f4SpMgijm+LJgAWVNMbqI4Yalmk7VUCpbO8/sqw9fZ\nN5rhUs2mkjEYz5m8udlms+WSNhSemhed2D9+cZVW32e6mOLJ2SIvLzfZaDm8styiYXt03YC2G3By\ntcX+sSwLlfSQdXB2S5g/lFIaYzmT2XKaA+NZql2X75+vESegSBL7xh4GY75bcGmnx8vLTf7bn33k\nx85X2jOS4R999lH+6z95jd/5zkX+859avKPfs9ro88v/z49I6Spf/MJzw8Dbv35kgn/wxyf47a++\nyaOTOT64WLnJb3rvousGvLLSJElEA3pxVDyLAHOlNLL8lqtuFCe8eFnkeO02a85v9+g4Ia1+iAzX\naIBkScL2BbuikNJ4ebnJxZ0emy2XuXKKRyayXB6sj3tHU0RxTK0rGEX/+uVVHp3M0ez7NJ2Avh9R\nbbtsdlzWWw4yCWe3Oli6iiZLTBYtEhLGsyZ5S2WjGbPWdDiz1eG5veIzbjmBoBiTYOmKWHeS5BqW\n0LsJt1P8rEmSVAD+DPi6JElNYOPeXNZPHrtuWFEkHDxudfqzfyzL/rdtHme2Omx3PJ6cKxLGwoY4\nCGNkGVRZOGj4UTzQ3GicWG3RdQMOTuRwI2FSEMYJXhAPF8A3N7vsH4MgSug4g2uNk+F4FeD0ZpvX\n1tp899wOrcFNbmgKl2t9wjghikWmj6UrLI5msVSZQlpnPGdS7/kU05oQ24axGHk2HRQJXl9vkzE0\nOk6AJElsNB3BTY0TOk4w7Iz+OLjfQZ0/SewaBDhBSBwnd5QP0HYCXl8Teh9Tk3GDmFJaFCM/uFjj\n0o5NFCe4QUjW1Oh4YvEqWCpxHOOHqrCuTmCn49HqC9Gj68c8OSeyQRZHs+iqQjwQskmDCMmJvMUT\ns0WqHY+8pbI4mhm+h/fT5/heRBwnrDb7yJJ0FS2q3Q9IkgTbC/GCGEmGWDAdSBD3hqZIJEDG0Dgy\nXeBThyfu6rXt6mTiWHROVUVY8YOg5lq6ct3Nd1cbB3B0+ua0jCCO6bhiitn3hb11xlCJBs9BnCQk\nwOeemBr+zOGpPGc2OoxmDKI44dRGh7wl1sz941nMK54jEK6Llq7w9HyJWs/j3766xhvrbTH577q8\nvNxgMp8ia6q4YcS+0Sznqz1eW23TdgWbIGuqhEnMQjlN1lRpOT6jWZPJgkUlazBVMFmq93l0Ikcp\nrbNvYMrQc0Mu1WySBOYrd/ew8vD5//HwJ8fXUGSJz19xb/04+MUnp/nOuR3+96+d45mFMk/OFW/r\n520v5O/94ctEccK/+vVnhgd6EIY3//gXj/LmVof/5kuv8fX/8qPvWxp8HDPUe4dX2E4D19DRwzjG\nDSJsP+TcVpe5cnqovZ0ppfjQ4gimpnCh2mW95TKWNTgwnuXpuRJuEFOwxFRm9ykLowQFiBNxxjy7\n3eXijk217bHRFAXQRN7AD5OhZfVW20ECcpYqGBtSSEoLyBoaG4OwaC9M+OVnZgmiBE2Rha5ncF7J\nmirjOZO8pfHoZP66tMp321pwO25vnxt8+Y8kSfoWkAf+8p5c1X2AcKjwWRzNXDcMcpeultKVm4ZF\nvhOiOGGtIdwxtjouH943wkwx4PRmh+kBV9NC4dNHJwmiGC+IubhjE0Qxf/nGJpP5FEcn8xi6wrFB\n8bTW6LPVcfCjiDCKeXZPiShOSOkK0wWLl5camJrCybU2x5ea7PQ8wigmSRJURVTyXhCTNRQadsCR\nqRwjOYtSWmdxNIOhynhhPDRMqNseO12XjK6wVHc4NptnomAxW0oTx2D7IRsdB0vcD7QKAAAgAElE\nQVSV8c5F6JrMswsV8ncgMt7tlvb9kMNTeUaz786R6fXgBhHntrtoisyBsewtFzGHJnOsNvqM5807\nDkbbbDu4QYTTD8XYOmvSdQO+fXYHXZWxNAXHD/HDiJ2uL7rEjkolo3FgLEvKUNloudRtl1pTuEAp\nssxEweTYXJm5ksV4PoUMGJqMpavDz/fIdJ6RrEEprTFyxee11XY5vSkK5SfnikOh5MOE+DtHreex\n0ugzljOZege++N3ChZ0uL11ukjVVZElYPHf6AV4YMpY3uFTrkQBhBLssjrSpMZo1UWWJtK6QNjSe\n33v3u8CLoxlUWb5qjX58poDtRUwVrRveZ7v5VCCoyjeDpat89rFJXji7wwcWSkM7//lyGgk4vdnh\nzKawlN5NTNcVmSCOsf2QQkrF0mTylsZT80UOTebZ7riMZg28MOLEiggFzlkaT82XuLRjszpw8nSD\nkAThrNR2Qp7dW+ax6QL9IOQrr20yW0oxVUjxxIyO7YUcmcpTswN0VWapZnN+u8dcOcVM0WK5bjNT\nstjoeOybyLF7JDM0hf2jWcI4ZiRj4gcRay0HSZKYL6fu6MASxwmvrDRpO6KJ907ahoe4PqI44Uuv\nrPPR/SN3jTokSRK//fkjvLbW5r/4o1f4i3/w4Vs+38Rxwm/8mxOc2+7yL371A1cVPrtI6Sr/8984\nzN/+vR/x+9+7zN//2J1Nlx505FMah6fy9P1w2IBxg4j1lkM5rV/1NzdUhUcmsnz99DbltM7J1RbP\nLJRIGyopXSGf0ji31eHb53aII3glFIXSk3Ml/trBMdwwYjRjMJY12O4I7a2lq+wZcdEUmc22S8Hy\nafd9NFXG0kST3fZcJCmhYBn8/ONT/NmJdSBmqiAyxZbqfYI4odHzCeKYjKHx5laXo9N5Ltdsul7I\nt89Vh+e05/aWh9qgk6tN/ChhTyVN2lB5ZbmJF8U8Pl24YexLGMX31Yr9psWPJEkm8AVgEXgd+P0k\nSV641xd2L9H3w2EI6IVqj8NTeWHDeoUJgaUrVxkM3CkUWWIsZ7LdcZnIiw3gBxfruGGEG8TDTUGR\nJRRZQVNksqbK5ZqNoSpEcYwfxaQklZ2uhz9w/8gYGk074NBEDkNT+OiBEaodj6+8sclGy2Whkqbd\n91lr2khIZE2NlKGgqTIHRnMsjqU5vdnh3GaXs9Ue6y2XfWMZtjsuhZSGKkuAxHbHoZQ2kCWJYzNF\n7CDi0ckC0yWLJIa/ulSn6wa4fkS17fHCuRqyJLHZcvnMY5OU0vptbZ5dNxxOr7ba7gNV/Kw0+kPL\n72JKv2VRqe2FQz3XrWKn66Ep4ifWmg6yBKvN/sAEQ7hCffNMlZWGjaWp/NLTM3zp1XWqPY9a16Xj\nCCe3Rl8jSSQmiyl0Taba8TFUmfGcyZPzBUxFRUbozD60OIIz6LZnr3hW8pZ2TdoziIIsjqHjBHSc\ngAs7Pdr9gP1jWWYfUuLuCLuH7KbtM54z71khuevWc367x1qrT6Pnc3mny9ltm7bjM5UzOL/dJQgT\nwsHER5ZlcpbGo5NZnt9boeUGfO98HVNXWGn2ee4uX6OhKhwYv3rKXs4YlG/gaL1SF6Ge0yWLvaMZ\nJLimgFyq2Zza6FBK6zwxW7iicy3x5FxpQOWLh66dE3mTl5aa9P2IbE9lcVQcEk+tt1mpO0wUTKJE\npKV7QcxiJYMqS8Pm2onVFme3uuz0PA5O5Kh2XCxNRlUkwigma2jUbJsgSlAkMBSJvh/y+nobL4w5\nu9Xh735ogbYTYGoqaVPj0FSBatfltdU2SZLwzTNVZCSadkDPC6lkJFGcRTGKrBCEEVlTZbpk0XV9\n/uT4Kl0n5MmFIvog4Pp2YfvhUPO32XYeFj93gO+e32Gr4/I/3mV76pyp8X/+rSf4hX/2A/7hF0/y\nu7/81C3t0f/kG+f596e2+e//+kE+sv/GAfQfWqzw8UdG+d3vXuLvfnD+XREA/5PA7v6fJAlrzT5v\nrHdQZYmVep+P7B+5au2eLqbYU0nT9UJkSUzRdotLEVTcp9pxeW2tzb7RLGsNhyOTEZoqD4uJqWKK\nWs9npeFwdDrPhxYrhHHCcq3Pct3G8UPWWw4zpTSnNrqcWG2hyrBnJKHW9eh6IRLQ9QIUGRYqaV5c\naiBLohB/dk+ZrbYrrK4TYdQyXbKodjxGsyaSJNFzA354scYb6x1mSimiOGG+nB5mUG533auKn74f\ncmZLGOPsnpUfm7k/8Ri3clf+ARAA3wV+FjiEMD94V6Pe83DDmInctd10Q1VI6Qp9PyKlK/zwUp0o\nStg3lrknoYpHpvMcTnJIA3HZ2a0uXiiC994ORZZ4Zk+Zw1M5ji+36DgBcZLQ9yO+8eY208UUOz2P\nkYyB7YW8ttZmqd4nTmJWG84wcdzxBVVvLG9hDygVHTeiaQd0XJ/p4gg9N+DMRpfVuk0la2LqMlOF\nFBe2XV4ZUO/GcxaPTuZxgxBVkbA0hWJaZ73p8P3zNTquz3wlQ1oX9qon11qUMwbfu1BjsmAxU0pd\n933eCHlLo5gWXcz70dm+m9gtnmVZhH3eCuI44eyWMI9wgi7ljMFm2yFjqBRSOj0v5NJOj4KlDwuG\n19da/PtT22iKxJ6RDBtNhze324xmTBYqaWo9j5eXGsIoQ1NJGQqmrjCWM1lt2Ni+yFyJY5D9iJVm\nnyhJKKV1VEWmlDF4frHM0ek8P7rU4Kuvb3FwMkvO1Iab5OOz17rPvB3TxRQdNyRnCt3YrhnIdtd9\nWPzcIXKWihtEpA31tgofN4i4UO1havLApOTGP9v3Q350uUEUJciy2Pg22g4vXqpR7QoHoVcQ057w\nCkaHqcpM5C0+eXCc0awFDZvpYopgkAf1k0SSJJwbmLRcqNpDTeWVcIOIk6stzld7pHSFrKkOG2Be\nGLM1CHhWFZmn54s0bJ92P8API7qumLqAaEKc2uxwfluEBT+7p8x6y2Gn5/K9SzUyhsqTsyURESCJ\ntPQ4SbA0Ma3xo5hKWudlX0x9ojhhumCiyjIJEpttl+W6TasfUsrofOX1DfZWsnhRRJLEohlhaqR0\nhZ2ex3Ldpu+FSFLCXMliJGfyyLgIfa12XS5URSMwThJ+cLHO5ZpNGCWsN/vMFC1ypnpVs+NWkDFU\nRrIGLSdg5g4ySx4Cvnh8jUJK4+O3GGp6Ozg6XeC/+7mD/KMvn+Z3vnOJL3x07zt+/1de2+SffOM8\nv3Bsmv/s+YWb/v6//7FFfuGf/YA/fmn1lr7/bqHV9+n7wmTgTlkUt4OuG9DqB4zlzBvafK80+pzf\nFjleI1md8g32TU2RsN2Q8bdN+Sxd4XLN5tWVFnXbJ5EEXfflZdF0mSun2DeWZbPt0h00jo8vNwhj\n0dw5Mp3n8JQ4f57Z6rDRcji33WG1aaNIEqosoaoyMhKylLDectluufhxjCzLGJpMztSwNIW1Zh83\niIXhkSQRJjFPz79lv74bqGxqMn4YUUxp2J6w7y+ktOEAYBfLddFYO7fdZbqYQpa8O6b+3y5upfg5\nlCTJEQBJkn4fePHeXtKPj3Y/4NWVFiA+jLeHbu4WGF4Y4QUxGy0RhNe9Qi9zu+h5IX4odBbXw+5h\no+9H7KmkaTkB+8ZuHLqXNjQ+vK9CEMW8vNzE8SNKafHQTBctxnImQSQS1HteyEZbZLPEMXz0wAiH\nJvKcWG3y2mqbtK6xb1yEWo7nLEppgyCMaTsRbddHVxUkCZZqfbY6Lmv1PtWehyQJulQYifyWjhcy\nmlWZLVq8sd7ixGoLLxJOcp9+cpZW3yeOE6pdn5SuIEtCBH07UGTptnnI7xaM502ypjiU3grXOY6F\nVXA+pdHuBxRTOme2Omy2XGQZnttT4dx2l0bPp9rxKGeEg8pmW9yvTTvghNNktdHHjWIsTeXURof5\ncoo40ViopDm73aXjBby01ERTJFRFoZw26CohaUMlTsQ9FIQxQZxwYDyDrgjNRJxIbHc9wiTBD2Lq\n9luOM7YX3rT4GckafDQrDplJkjAxsMN8aIRw5zg8madbDofuZreKpbrN1uC+yVnaO05Uu25IFImq\npt7zMVQF2wvpeiFeIPQtCbBLGJMRlLcP76vw6x/Zy/6xHLoqs9bskwB9L+Rjj9z9A9ztQJIkimmd\npu0P8yneDl2RyVkqIEJ7dyerYRSTM0XRuVBJE4Qxb6x3xPTU9iildSby1nDtlyUJP0wYy5mM5y32\nj2e5sNMjTmC96bBvNMt212GpbmOoMo9O5XluscJO12Wp1me5LtzcRjIGHTdkPGsyWxbrthNEbLYc\nDFWmmFZx/JCUrnK+2mWr7fINN+Dcdo+PHxzj2T1l/uLUJo4vDHUm8imeXyzz9EJ5eK2GItZ+P4xp\n9QMMVbAPxnI6lYwpKLL9Js8vVm7LBU6SpPvWwX0votX3+fqpbf7WM7O3lPFyJ/iVD87z0nKT//Wr\nZ5jIm/yNx6+vKzqx2uI3v3iCY7MFfvvzh29pSvTkXJFnFkr83ncv8SvPzd0XOpPthRxfFiYDPS+8\nRnt9t7F7NosikZ23Gyp6I4xkdUZzJo8PgsivRJIkOEFMOWNgv61RZGoKk0WLclo0E8IwZqMtJiiy\nJNEcNBULKY0EocepdX1qg8bH3pE0kiSs8dcajqDSJgk5U2i6p4opFkcyzBQsdFXmG29WuVzvI0tQ\nyRqYqowkS1za6ZExNSYLFuutPkenC4J2fEWjdzRrsGckM2h8W/S8iDc3Oliawnw5fQ1DpJDSWG86\nTBUs8pbGfCV9XwofuLXiZ+hXmyRJ+G4SLN0IVwpJ47eJzXahyBIpXSWlw3wlTd8PrymSbhVdN+Cl\npQZxzE0pPTNFCz+MUWTYU3nn15MkCV1VeG7gZOSHMestcQNvtl2OTAvu+Fw5hSzBJkLP8/hMkfWW\nQ932WRhJ88h4lqylMZm3CGNB7zM0hSCKmS6mcIOYKIrxgpCdjo8kSaR1lTCGR6fy7B3NsNV2RZhZ\nTxyIiykdTZVIJIWeF6ErYrN7dCovaE5uQMcJWRhJ4/iCo1q+TQrcg4hbHfEHUcyLg0T4faMZDk7k\nSOsKpzY6gBBLJiRkDJVGz0dX5WFn6cm5Ik3bZ6PtsFhJs9bsUzQ1WrbPZNHi/I7N4zN5Hp8p0vcj\nlmo29Z6LpSukdZmCZTGSjflPn51juWFzYqWNrimUUxrNwQEISWK6YPLoZI7xnMlozuQTB8eo24LW\nd7sJ1JIkPcz8uQuQZem6FMObYbdYkmVumsA9kjEYz5u4oQhmPr/VFdljEaQMiTCW8KMYFZAlSBkq\ns6UUT8yXOTz11oF3upjiP356Bk2R3xX2ycdmC7hBjKld/1pkWeIj+0cJo4SeF9Kwxda3VO9T7wma\nYcYU77XvR3ScgHJaZ6aUou0EAwFwzHjO4K8dGmWz5TJZtHhluUmt6zNdtOj7EWM5k74XUev5hFHM\n4miGdN7EKqXpexEd1xcBwZKE6wfMlgUdZqpgCXObBDHN1VQURXSBX11u0nYDeq4IRF1tCvv6rZbg\n/ectnYMTWY5MF65q0OVTGuN5k7NbXVp2wFje4FO5MQ6MZ0U+kO2TJAnJ9bfRh7hH+POTG/hRzN98\ncvqevYYkSfxvv/gYta7Hb/6bkwRRcs3rvbTU4Nf+35cYzZr887/z5G0VYr/6oQW+8K+O88K5HT5x\n8M5tum8V0RX3aRjdnxs2ucL05EaYHawPzb6PFwjzqLdPUiVJ4sC4mN5cb1L6kX0jeEHEDy+o5FIa\nEwWTckYnHBhNvbHepmn7kMCeSpoTqy0ag2blVttlpdHn7HaXjKGSMzUOjOeIE4mRnMFU3sRUFT52\ncIyWHXBuqysmTAmkdXVYAMmSTMZQMVSZQ5N5TFVh72jmqv1EkiQWR98617qBO/xaVa49+03kLQqW\njiJL9z0g91ZOao9JktQZfC0B1uDfEpAkSXLrnKb7hGJa5/BUHjeIbim47coP607ghYJCBAw1ETfC\nbhjd7UCSJBRJjD8tXRXuIbGgkgzzfIKIrbaHBJiaTK3nkTM1xvMWc+U0+8ay13QbyhmdlYbMhxbL\nTBdSfPNslXrPI60pnN3piZHpVIEP7q2w1XFZb/VpuwEXqz00RfDGd7oOq3Wbf/y1s/zs4Qk+9eg4\npbROs+8jSxIk8KPLdcIoYaaUuu33/l7FrmsUQKPvMzfg9x4Yzw4oNxopXaWY0liuJ8yVU2iKjONH\nvLHRoZI1WBhJ88Z6m2LGYDStkbMMzm13ubjTQyLhA/Mljk7niYnZ6aWBZNj93z+Wpd7z0RWFI1N5\nLtVsFEncEzlTo+eFQhchSzh+zL6xDKamMJK9uTXwQ7z7MF1MkTU1dEW+bvjtlbi406PW8zg0meXF\nSw2+fqZKxxXubllDZc9ohuVGH9cPGcnopAydpxdKZK7ze29WaN1PSJJ00/euyBKmLiIFdt3kdlPL\nTU3h8ZkChZROEMXD57Tvh6w2HBp2m9fWWpiawsGJHAceyXG5JvQ6MyWLSsbg2GyBGKFn3Ol6XKrZ\nxEDHCzkwlmWpYdOwffaPZdkzkub1tTavrbf5+cenMFSFjttiPG9yeCrPXDnN4miGKI7pOiHntntI\niIncRtPhdD9gvpJicSRNylDJWdo1lBoQTbUoTnhzu8OxmSKPzRbImRoT+Yi1pkMxpV11MEmShPPV\nHn4o1oV7NZl4P+OLL69xcCJ3V3TH7wRTU/jdX3mKL/zL4/zDL57khXM7/O1nZrE0hb94fZPf+95l\nZooWf/T3nr1t/e0nDo5SyRj8fy+u3JfiJ2dqHJnOY3vhfQns1RSZJ2aKNPr+O1L0d4NDiwOTA9e/\nvsnKdDF1w6ZiztL4hSdn+ODeCq+sNEnpKkemCqw1+1zasdnuuLhBRDljcGarK5x9VZnpkkUQxXTd\nkJGMwVbHxVBlfmr/CE/Pl/DDmBeXGvzlxW2yhkLbEQ2fA+NZ/CAmY4qMv08eGmWr67PWEBT5p2ZL\nyLJwAN1oOVS7HrOl1DXMp7GciTQDJNzQtONma/K9wk13piRJbunKJEkqJknS/PEv6e7gbiQY3yoq\nGYO9o5khLeJeopwW1Kcwjhm74j32vbde2wtj9o5kuLjTE5vf6PULDi+MmS2lafZ9sqbKL31gBlmS\n+eHFOi03pNbzWa7bfGT/CKosIUliWuZHMR03wtRkvDDiXLXH/vEcF6o9ntsrOqBLtT4gNtbdLszN\nCsP3E3KmymTBousGV90zmiIPcxOSJOHLJzdo2AHrTYe/89w8HTcYJsPnBraSUwULNxBd57NbHcI4\nwfYiXji3w2wpzeHJAh+YL/OtM1XKaYP1poPtCaFhOWMQxzETeQtFFvfE0ekCuiqz0/PZU8nctzH0\nQ9xb3MrEqN0P+ObZKtttly8dX8MPI5I4IYoTDFWikjXJmTqHJ3XafZE3tm80w7MLFZ6YfW/QnA5P\n5llvOUwM1teZUgpDk9EVeejSdOVzutYU65obxIO/k0K16zFZsBjNiuctpSs8Plvgwo6gHxZSGk8t\nlIiSBFUWTY2WE3CpaguRcr3PdDGFIsNkTjzfpzY6nFhpkpDw6aOT7BlJi/DhUFBMD0/nyZkamiJT\nTGui27vV5dhskSNThRseMhYGneKcoeEGEbryVrF3vcZgteuxUhfru67K95xe9H7Dma0Or6+3+a1P\n312jgxshZ2r8wa99gH/6jfP8zncu8eWTIsFEluBzT0zzW585dEfTZk2R+Y+emuafv3CRzbZzjd7j\nXmDsPgdqFtP6Dd3LroTYo8U6MVV8579DHCeD3Ef1GiZJxlSHRli7weggNMa7Z94wSpAlibQhGpuV\njMF216PjBBQsDUNVuLBj88yeMst1WzzLCcPA6HLaYHE0japIfOvMDrIskTI0jH6IF8ZcrvUomBqf\nODQumiabHZIEVus2pYzBWM646sz5bjWtupttuW8Ax+7i73ugcK+Lnl2YmnLdIMyFSppLOz3KGQNT\nU67p0odRjBfGVz1M4zmTk20RsLna6CMrEqoss97qE8UxXhDRdQO++vrmkPpQSGtoikTXjel5EaNZ\nE0tXyJliHBrFIuRKkSUcP0JVJPaPZ+i64X37G72bEcUJTiByQg5NvvPQNIoT4kT899JOjx9erHF4\nSthKi+TmFFOFhItVm5Sh8C++v0QUi9ygxdH0UNvgBuJ7P7Q4wlqjx07PQ1MVUoYILp0uWtR6Pn4U\n8zOPTlDJ6pzb7vL6WnvYZX6I9y7iOKEfRKR1BUOTWW/0+dFSnTCCqbzJeMFgJKsTxwmmrpLWhf4v\nQTR+8pZB6RYPAQ8CrnegeacNfLqYEgWDBNttj64XMDfoPKcN9aog02rXpWZ7OEHIsdkix+aKIlk9\na1JK6yQkOEHITLHIUwslum6ABEhSwhvrLS5Ue6QNhdMbXXa6Pj0vQFUkpooWHzsgdFUjWYML1R4F\nS8MJI+p9n1MbbZ6cK15DO15t9HGDiA/vq7BU61NMa8NJ142Q0hVkWWSZ3K727CFujj95eQ1Nkfj5\nu5TtcyvQFJnf+NQBfv0je3h5qYEfirDeH7dg+U+enuX//vZFvvTK+vvW9hoEpfZWw4PPbHXZaDko\nisQH95avmqx2hrp0ibbjszia5Zk9yqDYEc9iux/w5laH0ZxB2lCpdT1IRGG424hOGyptJ+D8tjgz\nIgl6XrXjESciU/Lctk0Qx8zkUthexHwlzfHlJqqsYPshxZSIP7B0hb4XsdPzafQDNtsO8+X0fbWt\nvhPczZXrYXv4J4jxvHnNtKvjBqzU++RMleVGn74XDgNNJwsW85U0xUE68FLNpmn7qKpMJW3Q90Ki\nGE6td3B8wX/PmRqOH/LmZofRnMlkwUSTZSo5k4mcSTmtc6Ha46n5Ek/NF/nu+dowST2IYr57rsaT\ncwUsXR2E8t1+N+lBQhwn9PyQtC5MEJIk4aWlBj03ZKpo3dQFT1VkPvHIKK+uNnGDiO2ORyntXCUk\nTpIEN4yodlzeWG8Nx9rTeYvD0wVcPyKIYr706hrjWZOdnsdUITXkKKd1FT+MWRzJkNIV9o9n0BSZ\n752vXVMsvx2rjT5eGDFXTr8rNB0PcXO4QcTZrS6mprB/TLi+vbLS5NJOD0UWlFxTl4ljoWXsGQoL\nIxkato+UxOiqxkrToZDWhfasHxAlCVsdhzjOv2+nhKM5Ey+M6Dgh43nzuoVgzwu5WO1xZqvLgfHs\n4AChc3arS63Xod7zsHSFybzFbDlFxlD58L4K379QH5oRjOZMUpqMrklsdhwu7fRID3j8B8eNId1n\nN/D67FZHuPypCmlDvWrNqXZcTqy0sHSFqaLJnhHRnEoSkCQhID9f7QmdgKVie6JRkjU1nttTIYzj\nm67h4YByk7O0h/let4AgivmzE+t84pGxG5on3UvkTI2PP3L3KGqz5RRPzRX58xMb7+vi53bghWJC\nFEVi4r6LMIqpdUU2TzFtYKgyF6o9Zkupq2ipQRyT1lWWajaXaz0u7/SHmqOP7hvh6GyBhXIa2w+R\nJNE4f35fmZVGf6jPDqKErKlzYExlspAiCGO22g6jWeEy3HZCzm3bTBXTPD1fwvZCel7AhaqNG6gE\nUcKtsGF7XsiFao+sqd6x5v5OcTeLn4eSyHcZzmx26TgBl2ohmixT7Xq4QYQfJliaQj8QXPWFcgo/\niimmdZbrNlMli4XRNF86vkaUKGy2XNKmRscLsf0Q1485s9FlrpLi0GSe+ZLFctMhjJPhIViVZZIk\nIU4SVht9Vup9bD9io+UwVbSQJYmjMw9WiOnt4rX1NrWuR87S+MBCiSASB5KuG3B6w2ehkqJuB0gk\nTBauz/VdGMkwkTf5gx8us+V5lDI6V5IhwsEhp277qIpwXtEUmaWmw1jB5dBkjr+6VGepZrO0YzOe\nNwdTvZiRjMFGx6GY0imkYvKa4Pb33BBDEyGNNxKHb7T6nFpvoyoyccJD6ssDgss1m52uhxtGAyeg\nDC8vNTm52qTlBHzmsUmattD3KLJEIaUzkjPoeSG1TogfO6QNhe22y1Te4qn5EnlTRZIkWo5PwdLf\nVwXQVtsdakvf3OxS63oEUcyekTSTBeuq4qBhe+z0fBRZQpNFkDSAF8R03YD1tkscJ2hyOPw5exBZ\nECcJ+8eyHJ7Os380gyxJfOnVNbpuiK7IhHFyVfNrZmAxPl9Js97so8jyVbTjIIp5dbXF2e0OYzmT\nlKHQ94QeUJVlZsspzm93eXOzSxAJ++y0odL3QybyFk4QMfE2ipHji5DnKwvrl5eb9FxhyX3sJm5Y\nt4JdNsF7tdnyrTNVaj2fX3zq3hkd3G989vFJfuvfneLMVodHxt91EvF3HQ5O5Fiu98lbQqO52uiT\nMzVeXKpzcrWNpkh8/tgUpzY61Hs+my2HZ/eWRVCyqfH6eptqx+MHF2rMllO0BvbYqiyLzEhdQZZF\n9uOx2SK2LxwlX11pcWarS85Uma/oJBIcnMiiyTJfP70tJAyJKKx0VaHnhnRc0RQvpHT2jWWFy+3A\nrOlWcKHao9b1qHW9AYvg/jXEH86s30NYbzlcrPboByHThRSWJtNxRODmSMYgiGKq3Zi1Zp/5Soqv\nvrHF+e0u5bTBrz0/z3LdYW8lje1HAxe5NM1+QFpXSOkqbcdjq+PS8QIygy7ibClFlAj6Q7Mf8Pig\nW7XRdrC9iDAO+ODeMkt1mzgZ+LcngCToWbyHz8ydgXiwOxCL66rMXDnFN89UGckavHB2BzeIuVSz\n2Tea4ZOHxq5rke1HIoMnjmNeXmqgyRJPzBZJGyqaIjNRMDm92ebIZI7VpsgIKqY0mn3h1LRSt+l5\n4dB6PG/ptPGJE9FZyloqT84VmCqmBJ9YF69XsPTrdh8bts8rKy3Ob3fZO5J5zx5EHlR03YDX19sY\nqsLR6fzw8xHTQ5daTwjtbS/ka6e2cMNoIJiNObvZIaWrw079/EiGPZU0YZQwWbDQZJlz213KGXFv\nfPyRUS7t9GjaPq8st8hZGk/PX0uvei+iafu8sd4GIIzjQTA0LDdskGCz7VIq8woAACAASURBVPKR\nfSPDYtAPEyoZnSQWyee7VvHzlTTVrouhKYSxmMLuUpZ33dumihbP7Z0ijBL6fsiLlxvkTI3pgsXC\naAZTVXhpqcHhKaH7keW3XJeKaZ1W37+KduwGEcv1Pl6UoEgSiyMZXlsT70VTJdpOwGbbZb3lECcJ\nuipoNY4f8cpyEycIKVg6z+0tD+ktu4U1COOUclqn7wuajn2bkQfXw1qzz5nNLroq84GF0i3FCTxo\n+OLxNRER8A4hog8afu7IBP/Tl0/z5yc2eORnHhY/N0KSJJza6NDqBxwYzzKSNXhjvc1WW0Rf7IYG\nC01vyAvnqjheRN+P0FWZrismOboi40cRk3mTlK5waKJMteNh6TJHpvIULU00puOEpbowWIlisb5f\nqPboeRGVjM7fPDCKpav85RubdN0A2xfnSi0lM19JkzaV4ZoHcHA8h+1GrDRs/sPpbX760XGMmzyj\nOVPQ8jRVvmGj9V7hIe3tHmGpZrPRdpgt3djB42ZIBofTW+FOxnHCmc0Ol3ds7CBElWSemC0wWbBI\n6QoJUExpvHC+hqbINGx/SG0L45g31tpoqsL5apfJvEUQJcNsi4MTORZHM2y2Hc5t9+g6AbIssTCS\nZv9YljOD0NazWyJr4uh0jlLaGB6cFVlipmCx1nQYzxvoisxI1mD6JsK/Bw1vD+faN5pho+UwUbCG\nh8G5svibRXFCGIn8jShOcIOIhu1dMwFyg4gfXqrz+nqLtYbDaM7gtbU2lq4O85CESFnBjxI+d2yK\noqVzerONH0YcX27ihTGqLDJMdFWm2fcYz1nEJDy9UCZrqGQM0TFSZIkjU3me3VPGD+OhwPtKtPo+\npqqwp5JhtpRi/mFuz08cV957a02HvhfR9yIatj8UAZ/b7hJGIkzTVCS+u9QkSkTwparIKJHIcpKk\nhDCKkSSJOIaPHxjjA/MVGn2XsazJKyuCXrl/NEsprVNKl/jBhdrQ/llMgN9728GlnR5bHZf5spjq\nyFcUeJIk8ch4lmJaR1GkYU7SlSimNGZKKRbKaR6bLeCHEWGcsHckzVw5RRTHA7qJ6H62+j4rjT6F\nlDC5yZoacZzwp6+s8cZ6i/WWy+MzRSZzJroqePfrTYfcxFvdUzcQh6BdN6pd0bWuyliaTCWtM5o3\nGM2ZHJuTiZMEVZb4/oUab252iEk4PJVjcSQ7cBtV+OHFOq+utHCDmNVmn198StiZ5yyVjRYoikRq\n4Jh3eDLPZtu9K2v97uHPD2P6g4nYewk7XY9vnqny688vvOv1EreDSsbg+cUK/+7EBv/VTx+4Z42R\nOE44vdmh54UcHM+RT93dKUIcCybLvfpsbD8aurGuNGxGssbQujtJ4COLI5ywmlQyJpttDz9I6HgR\niiyyxeIkIYwSnlkoDb+eL6eJ4pipoohDWWs5nK/2QAJDVeh7Ysq8XLeH63bOUkkSifWWy96RNGlD\n5emFMmtNmz0jGYopnYVKmmJav8rNU5YlYmJWmw5RnDCeb/GBhWv16Vdiz0hmYKWtvCutrgGQJGkv\nsJYkiSdJ0k8BR4E/TJKkNfiWT9yD63sgkSQJF3d6JIkY691J8RNGMS8tNbG9kAPj2ZtaN/pRPNgg\nVRKEMULW1FBliR9dbnB+u0OzH6CrMvtGs0zkLT73xBRfeX0TS1cIYpDjmKyhESUxYRyTABN5k5eW\nGry01OCx6QKfeWyC19bauEHM+WqXEyttKlkd1w/p+wFdx6fl+Hz6yASWpjCWN/DChJSh4QQ2x5eb\npDSVTx4ae89MDLww4uWlJl4YcXS6QCVjUO26vLnVQVPkq6YnuipzbK5Ireux3OihyhKltEbd9vjT\n4+scnMzx/GIFc5DDtNV2ObfV5XKtT88VReeR6fxVomRdFUG34wWTatdjrelwodrDXWsTRPEg20kX\n4WrAyY02aVMUT4YqY2oKrb5Iqwfh5jRVsLhO3QMIt5p6T4RFPjqVv2ozi+IEWeJ90fl/NyBJEk6s\nCtOSPSNp9gymBqsNG0WWr6IRqLLEa+stNloO7X6AE4TEJHihoLy2nIAzm12ylqBOThVNOq7PDy/V\nSGka3z63ja4q/MzhcT72yCjpKza+xbEMl6o9JgrWOz7Xu25iDxo1TpiO2ICwAp8sWORTGo/PFvDC\nmIlBovxUwcJ2A85sdTk8dbUGqu9HJDHImsT57R6vrjaFjidroCkyGVPlydkifhijqzLLjT6nNzvk\nTW0YiB3EMZos4wQxlqZgaRIjOZOOEwzT1XdxfKnJVtthvGANssE8/upSHVWWkSWJMBaTqJliiuPL\nTXKmKpLiWw47XRcvjOg6AWGUsDiaIYhFNlnPDYjimEJKo+uG2F5IIaUzXUxRSOloijQUadt+NHS0\n+3ExX0njhdEwAuC9hj97dZ0oTt5TlLddfPaxSX7ziyd5ZaU5jOe422g5ARsthyhJWG7YHE3dPfdJ\nL4x46bLY449M5W9o2/zjIKUpZE2VrhsO5QAHxrOkDYWcpVHJGPx0YQKAb765TZjE9NyAIIwwVInT\nmx0YNLOCKGGmmEJTJDRF5cXLdVYaNpIkUbB0yhmdR8ZzSIiC0Q9jRrP60PY+iGOypsLx5SaNnk/D\n9kjrKqoscXQ6TyltDJvziixhDyQRs8UUJ/QWKU29ZQv83E9I+307k58/BZ6SJGkR+H3gz4E/An4O\nIEmSxt2/vAcTkiRRyRjsdL07zkXpB9GQKrDT84bFjwgPDRjPmaw2HS7Xerh+hK4qZAyVnzkyjqHK\n6IPk7peXmvzxi8uc3uyQMVSemi+xdzTNVsclCGMOTuRQZZlL9R4qYppzaDLPa6stql2Xb53dYbvt\nUkrrKLKwbIwioRn62uk6qiRTtz3mKxkUZFRdIqUp/OBinb2jGZ7ZUyKME3a6Ll0vYKfrsXckzU7X\nwwtFp6PrBoxmzXuyoNwPtPvBMLOn2hHc1VrXJ47Bi2M6TnBVlzJvaZAImsh0MUXXDfjOuR022i6b\nHZdD4zlKGZ2vndpClWW2Og5BGFHJGHzyoKDG/fBijWZfFCBfe2OLtWafhVJKLIYRrDf7NG2PfhCT\n0RWqisdsySJBQpVkMgNHvtMbHQ5OZimkdGS5jyxJFG7Cu+26IV0vQJYkgigeHnY32w6nB2nOTy+U\n3jPF7bsZQZRQ7/mA0J/sGclgqDLKQHPXdUNMTcEPxX3YcwO6TkC95yHJEllNoWBpJJKEJkPN9vEi\nhbyls9roY6oKX3plHS+MqfU8Shlj6PJ22bVpuwF7Kmm22i5120dRZGaKqesWNxeqPZZqNllT5en5\n0gNTALlBxInVFhtth3JKZ7r01hRjl7q2izCKWWk4pHSVzbbLeN5kud6nmNJp2CKsOIoSNlqCFuwE\nEW4QsXckQ73n8cXjqxiqxN6RnHimy2nO7/T45pkq00WLp+dLfHh/BV2TAbEOt/sBQRQjy8KERJJg\nuWbzF29sUk4bPBrFPDFT4EeXG5za6A7cnCIypooXCEv86aJF01aQJThf7XGh2mOj6TBRtIjjhLrt\nE8cJ602HIIo5MpXnYs0mShIatj+cEO+6vyVJwpnNLi8vN4T1d8flpx8d/7GaIhlDvWcH5580kiTh\ni8dXeWK2cMNoigcZn3p0DOPfynz55OY9+wx1RR7See+25XV70FwAcR670VllN/T0Tu5zWZZ4Zk95\nWFAAbHdc1poOY1Fy1VrjDSJEdo2QXlltsdPx6HohLy41OTZb5PX1NguVNCMZnVrPw9IUlhp9xrIG\nm+0+y3WbSloXOkRFEvq/rImpKaw2+uiKzEbLYa0pjFXmB7TZF87uMFU0qfcCEhKOTBW4UO0RxSLL\n8fPHpuk4wR0znu4Xbqf4iZMkCSVJ+hzwfyRJ8k8lSXr1Xl3Yg47HZgp4YXTHAXBZQ2U8Lzp6u7ap\nXhhxfKlJFCc07YC2ExDHcKnWZ99ohp4nDjq7r1nreZzZ6giBcxDhhTHbHVe4dexSCKIYRZaIwoSF\nsTRRLHiYAMeXW8iIBPc4iVmu9zm51iRnacyW0kzkLF5dbXGpZrPedDkyneNzR6Y5vtyk70dcqPao\ndl0mCynmymk+uKfChWqPRt+j1ff59tkdLu/0iBPR4Xg+pT2QgXnCGlfDDeKhh/9MyaLtBJiaLKwk\n34acpTJdsrhU7bHecji73RUdlyhC12ReXWlyfrtLtevR7AfsG82SNjWOzhT4D6e32Wg5fPf8DmN5\nkySGuh2QNQNGMiZzJZPleo96z8f2IuIE8qZEveuj68qAqqTz5ZMbeH7E987X+Mj+EebKKfaMZIYL\nbxDFw3ynK9GwRWEXk9B2guHou9rxSBLR4e654XvG+vjdDF2VmSpa1HreMCi344Z4QYzthzRsn5Gs\nwasrTb53ocb3L9TRFAnbj0Sn0VIZy5msNIQhSQyokkQYxRiqTM8PCRNxyPZD0Wm8XOvx5yfWh+vA\nd87XaNo+fijWEi+Mr5spU+sJPUjXDfGjGFN+MJ71asejZfuM5wxmy+l3FG0rskTO0ug4AcWUzpmt\nLo2ez3rT4ehMnmAwoddkCUkSjo4zgwbIWqvPy0tNum7A0WkHS1NQZLA0mXrPx9IUtjouj4znhhlD\nJ1Zb1LregNoqCth6z+PURgfbjZAkQXuUZQkJ0TyrdlwOjOWodV10TSEIYxw/4thckY4bUu149NwI\nS1dYazhEUUzGFPrC75zfodMP+OSjoxwaz2PpCrWez563SVSqXY+VRp/VwX11YCzLWtO5L+GTDyJO\nrrU5t93jtz935Cd9KfcEWVPjYwdG+crrm/wPnz50T5z//ChmTyU9pG7eTZTTBuWMLmIjbnAP217I\ny8tN4iTh2GyRvKWxVLNZazpMF61h8XAzyJJwYrR0heV6Hz+MWW302Tvyln20pspkDY1SWkdTFR4Z\ny3JhW4TPp3SVjKkynjPZajusNftYusK57R4yEifXWsiSyGhs9QPcMEYGHhnPEsYJo1mDjhuKZpkX\n4gchsiyx1uwTxwnVrsu3z/p03JDFwTqkK2It98KI0Wz2gTCyup3iJ5Ak6ZeAXwE+M/h/773Z813E\nj3OQlyTpmnyVJGFoURwlIjX80o7N4zN5VEWmkjGuek1LEwfdkawwO0ibGo/PFEgbKoYmuPnPz1V4\nY71D1lI5sdJCV2TWGn28SGyImx2XmaLFoYkcpzY6vHi5yeJohsdninx0/whntjvkDAVNlcgYGj0v\nYLvjkiQJ+8ZKw4e1kjEoZXSmQovZSgpTVaj2XDRFULYUWUJ5QKlSmiJf082yvYjRnMFsKUUQxfzr\nl9bo+xFPzBZQZJkgjFht9um6oms7V04hSRLPLJTJGSphnDBREPk7j07miElYrGRoOwGFlMYPLtaG\nhdFIRqeY0sjoCilDppLV+exjk3S9FQopoRNAEkFox+ZKTBYtFkcyvL7epu+FBLFwnar1fPaNDUTb\ndZvz2z0ypsoH3talny5adJwAVZEZuaKwmy2lsL2QtKHeV9eW9yO22oJWWUzpHJ3KX2Vh3HV8Xt9o\nkzVUOk5AFCec3+6yUhfp3FlVoedGGIM8sJYbULM9ipaOIsuUUxpBLAqe2YzOvtEsZ7a7aIOwu0pa\nJ4iSYdGbM1V0RWKj7TKes24YprkbvFxO6w+UXqPrBpzZ7pLSlZty2CVJ4qm5Iu6AnnVqQ5gIqMpb\nKe+vr7fpJwmfenQcU1OGjamOK8xl+l5IfVDQlAehgaos6LMTuau1M4sjaRQJDqSyJIkIkh7LGWJq\nrEjsraR5aqANnCqmmMjZ5EyVfhBQTGv86FKTUkZnvpLCUGXKaZ1CSqOQ0ohihXwqIWdqbLU9/CjC\nVGV2opgL2z0K8zqaql5X86crMptthyCMKaU1JgvWUL/wENfiD3+4RMZQ+ezjkz/pS7ln+Mxjk/zl\nqS1+dLnOB/dWbv4Dt4mCJe4z2wuZK9/dTEFlYDL0TmjY/jCAvNbzyFsal2u2oMzWerdU/CRJwl+8\nvsm57R5TRZOD4zncIGIkawzPUm4Q0XNCNEUYGPzs4QksQ+HYXBFVljgwnqPacXltrUXHCVgYSTNT\nFK5vOUvn1ZUmbvhWWPrCSIa0rnJkukAQxfiRaF7JksRYzsANI7w4oZTSKQ9CmxkE3sckjGTNYWD7\n/F3+u99L3E7x86vAF4D/JUmSy5IkLQD/8t5c1kNciabt///svXewZvd53/c5vby93d63F2DRQQIk\nSNGUEIqiLcmUoyiJZSeK40zaTDL6I5PJZOwkM4nSxpOZaOzESTQT05Fk2YopS6ElNoGECIDoWGzf\nu7eXt7fznn7yx++9L7Zi94IAtnC/MzuY++K+9577nnN+5/c8z7fw7bM7RFHCyZkchqowU7AwNWV0\nk8dxMrJO3UPKUDk0lmGjOeDgWIr5UoqpvDBguPoibTo+hqIgkXCp1sNQFZJEiN8msibPHSpjKwrv\nrHdYazr0XF90g1WZmZzNTttjMmdyfCrL2e0eAz8iZaoijDOI+dGlGllL5/G5Aqdm85ze7BDFCSem\nstSHVJCZgvXAiDzbg2DkAuWHMUki6CLNvs+FnS7PLhZ5d6NDaxCw3Rb2lHNFi+cPVViq2Pzp2V06\njs942mD+1BRuENFyfLY6Lhd2exybyPDUfIEfXqpjawpff3KWwxMZ3lpr8vpKi622x6mZHP/W80uc\n2+7QH1phZyyhUZgYBtd9/clZVup9oiTG8UUBtoc916aeGzIIomvyfmxd0CebfZ9XlxvYhsqj0zkK\nKZ3nDn78D7WHuBHrTYcoSqh1PZxhaC6I87Zcc1Akcf83HZ//8wfLvLna4OJuDwnRREkbClKS0PNC\nUrpGztKxDZVn8yILyvEiOl7AY7N5DoxlWGs4aLLEWNbk0ZkcKV3lhcMVZEka2mY7fO5Q5UPpJpWM\n8ZFpwJ8E7oSi0hg6ui2UbFKGekdFmyxLo8Lw2ESWSsYgY2hoQ9va7baLIgl3zum8xY8u1wmjhPGs\nwbOLJQxVou4ELFdFePHhiQyfG95XV+oOtb7HYinFpWqPly5UKWcMvlBMjQTeG60Bk3nRTAmihFeu\nNHh6ociRiQwr9T7ntrskCZzb7uGHwqSi0ReU5GrX49mlEs8tlXhjtckbay0xVVIkLF0ja2ksyDIk\nElGckDa0m1KAcpZGwdbJzmpIkpjsP2gGNx8X6j2PP3p7i199ZvaBDo390tExbF3hm29vfSLFjyzf\n2DD+NFHJGGy1XaI4YXJoOz+WFcXC1U2L642RrsYeOyeKE6odnxePpzg+mWWl4XBhp8tiOcXy0Ewr\nZ2sslMV9f2ZLWMxPDqc93z6zy5V6f2R2dWg8jaYqVLsuRVujH4jG5eGxDIW0zqOzOcazJm+vtQgH\ngnYXxDEbzQHR0GHy1HSOjKXx2aUSjb5Po++TNhW6bkCzr3BiKntfaX33c6f9bJIk/9HeF8MCyP0E\njukhrsO5nQ5Xag4Alaxx0xCyN1abtJzghvDM3a7gjNb7PuW0yRNzhWtuvNW6CLZ6b7ONgnB3snSF\nw+MZbE3iR5fqNLo+E9M5Fio29b5HmMBWc0DaVFlt9pEkyFs6QRgThBGKIonuohfxvXO7rNQdZks2\nGVNhtpjisatCOifzD84DsesG/PBijVrXI0oSSikDVZYYz1rESULXDSimDVrD6c1Ox8XxI4opnXLG\nwA0ifvu7l6j3AxQJimmDnzs+jqrI1PuCwiZL4IYxL56cGNmJF1I6c8UUf/b+LmsNh6bjk7dUHp3J\n89kDJb5zViyEc0WbQfBBgXyzJPs9zJdS+FGXwtBp6mrEccJWx+XSbhc/THD8iNYguCuhfD+tmMpb\ndNyAvK1jawoXd7tstFxSunBsbDkBKUOl0fc4s9Vhq+MRJiKHq+uFwylBTMZU8cOIqZzFl45VCOKE\nP3xzQ4SeKjJvrbUFNVaRCYOYqbzF105dmzxv6+ptA3vvNXTdgNdXmgA8OV+4ZVjnO+stFFliu+3x\nM0dz+55YybJ0DQXEj2LOb3eRZIlHpvMidy2IuVjtYdQVvvLIBFN5i5bj89qVBtWOR94SlNp31pus\nNQfkLA0JhBi5H9DsB/hhzHROTNUlYKksJkIZU2W77fKn72+zWE7zzGIJTZF45XKDJAFTVxjPmiyU\nbM7v9OgMAjZaA45OZGkOQlRZJpMWtucZU2Mya1LviyT3IEoIoviGv9kLI3bawjSl4wXMFVMP6W4f\ngv/ntTX8KOavf3b+bh/KJwpLV/jysXH+5L0t/u5fOXHfaUJbjo8bxIxnjZtu8k1N4ZnFaxkgBVtn\nozmg64aEUcxyrc9K3WE8a/LIzI2FmqHKPDKT5+xWh6OTGXK2xnbbHZmtSBL4UYSEoDwvllMYmsKr\ny3V2ux4LJZswgiv1PrtdlyPjaaIoptb1RmHDOUsjY6osVdJM5ixqfY/JrEUhpVPr+lyqiuydybxF\nZ+ADEgXbYKPlMh5DNyPCmR+dyXO52qPlBGy3XRbKqfuqeN/Pkf468Peue+1v3OS1h/iYUUkbw01K\nzFTO4vWVBo4fcWIqRzGlE0bxyAZ0reHghTFFW2euJLQ2uiYcxQxVBN15Ycxqwxlqh3yyloahyEwV\nLJ6YK5CyFEopnX/25ibv73S5WHPY7rg8Pp8npSk4QUS955O3NS7sCMH7xWqX1sDH1FQOlG2m8jbV\nrk/XC1iu9VlrOhiqzK/kby6GfhCw1hhwZqvLdnuArQn76NmCjRfFHBrP4EcxMhKWrvCVk5Ms1/q8\ndKGKpausN91hkruPras0Bz4xCd94dZVySjgyZUyVnfaAt9aF1fBMXmQsrTYGPLsI41nRVVdkiWbf\nJ20oIundCXC8kIKlMVMQ4+meJ3QhM8PA2WT4vj18WJd+ud5nudqn6Yip3d71+RCfHsYyBu3BB+du\npe6QJPDGVueDVPAEXr5YZ6XepzkI0GSJtKHQ6gu+tqbKaKFEGEWsNft875wIwssaGpWsKcLneh7V\nrs5zS0XcMOYrJyaGOi/llpuX/Vj03y3Uez7h0I663vNvWfwYqkLe1pkt2hyZ+MmF6H4Yc3AszXZ7\nwFqzj6mJfDQJmMgZbHdcpvIWeVtnvpRivenw+6+vEUYJ/lBLdWwyy8mpHJN5k5bjj/Q+/+LdLQxN\n4dnFIo/O5NhsO7x6pYmhSjx/sMJ222U6b1FM6eRsjSN6hnJaY76QYnEsjb3R5v2tLsu1vnAAtDWq\nHZe5ks1Y1mC17nBuqEN8bCZPztJu2ml/b6NNsx+gKBJfODz2ieg7HhSEUcw3XlnluQOlB9Lo4Hp8\n7dQU//ztTX54scYXj4zdtePYMzGJ44RHZ/O33bR3hs2SJIG+n+LAUOtyO+x0XJGv54b0vYitoZX1\nTsflRJy9Zi/kBhFBFPP0QpGnFz4oovacXdebDlttlzAW2uJyxkCS4P/+0RXeWW+TtcTfEBNTSGnM\nFy38KEGSJRp9n4mcwWbLpdH3kICmE5C3+xweT3Nmu8vzB0ocHk/j+CHtgU/XDTg2kaM4bH50BiEF\nW+OttSZHxrN03ZD5kk3LEe6P72+2KaaMUb7YvY7b7liGOp9fAxYlSfrnV/2vDPDQ4e0nRM8Lqfc8\nxjLmrbnyYxmm8jaKLAlh7OoAELkdILQ9S0MHtfYg4MJOl83WgOcPlnlyvsDXn5hludZntmhj6wpv\nr7fYarlcqfc5Mp6h6fi0nIBaz2cyZ6CrCl4Y0Rv4uH5CK3TJmRoHKiFfPDpOrefxnfe3ObPZEY50\nEliaSrXrYekhta6LLLcYzxp85eQk3YEQzHlBTM8P75q14X4gdEtck5p+O4xnDRRZQpIlSmnBnUcS\n/N8kSYTLVj8gThIk4In5Iv/Blw5R63n87y9dxvFC3CAib2scKKfYbLmMZQw2Oy5RkpDSVf7icp0o\nTjg1nePQRIZ4SGF66WKdaFhczRVsyhmDUtrg915b4/vnd7F0QYFarvU5v93lcq3PkfE0Lcen64UE\nUcypmfxNzRmuxx5dqGDrnJrNU07r99W4+0HAenMguNeArStM5MyR09fAj+h5IdWex27Hpdb3yRgK\ntiEmBpqqktLF5Ge96bDedMibGh03ZLpg8+R8nhMzWb717vYwfE7QHL99dpd/+PIyc8UUE1mTzyyV\nbmhkBFHMa1caDHyRDzZ1j052J3ImOx2xEZGGFLSpnHnDdfzkfIGW439s5h1jGYM/fHOD9iAApJFZ\nSsoUdrazQ4ckN4j41uktfnChhhcklNMatqlSMQzCKKbnhTx/oMxjMwXObLX59pldaj2PmYLNa1ca\nBGFM34uYyJq4Qch2e8BcIcXv/XiV7Y7LlapDylQJI4vpfIp31ts8u1hAkSWWqw4TOYOVRh9TV1BV\niVMzeTw/5MxWh0EQoysyf+P5RVK6wvubbXY6Lk/OF8la2gfanmRvrXi4NtwKf3Zmh43WgP/iF47d\n7UP5VPDC4TIZU+Wbb2/d1eKn1vPoucJNd7vtjjbscZyw0Rpgaso1zb8oSkbXdXiTDK9bQehgIzKm\niCFZKKW4Uu8zmTOvWTv7nggujuKEY1PZUS7XwI+4VO0RxDFRklC2NS7sdocmKSFrdYe319r0vAAv\nijk4lkFXZTRZJkwSHE88C+ZKYu+30/FGU6DVxoC0ofDuRosXT0xwebdDz485UE7R80IKtsGxqQzH\npnI4XsgPLtZwgoilcpowiuknIiLh0HiaP3t/h/e3uhwZz1D6EEbJvYQ7ade+DGwBZeB/vOr1LvDO\nJ3FQP014Y6WJH8Zstlw+e6DEblfwPSey1z6I9wqjrKWJpO1AbFi9IKbnhkwXLJYqac5ud/j2+zsg\nCS7xIIg4Opnl6GSWthPwez9eY6XmMF20MFQZQ5WZylvsdl2Waw4vXahiqCoTeZPnl0ooikyr71Ht\n+5zZavNzJyZ4d6PFmZ0eq3UHQ5Mp2BqaLJFNmzQHHmEEY1kNN4h48cQE0wWL97c6HKykSev3/oRg\np+Py7jDtPEqS0UJ0O5TSBv/aMzNstz1URaKcFraRhiLTHQhqSrXr1iqcuQAAIABJREFUUe97FFI6\ny7UepzfadAYhM3mLZj9gumCTNVVShvjnBTE5TWapnObsVhvXj4iThMv1Pk8tlpgv2ziesEX/5rtb\ntAY+9b7PM4tF+l6IIkl03JCWI3Q/ZUfYIov8n4TucAIEwnp7L1RxsZwiiIQ9ct7Wr+neLpbTaIqM\noX7wgIhiQevLmNrDTu8niI4boEgStiHWA0kSxc9kziRjqFR7HrqqMJ0XDnB75yJBwlBkojhGkRMm\ncjqrDQ8VGS8SznD6IGC2AO1ByGTWJIgh8CNKKZX3tzvsdDw2mgMsXRglVHseY5lrKSC1nkej52Nq\nCrtd754tfkxN4dklsd6+sza816OEuevE+7oq79uCv+0EdNyAyZx5w/QrGAYPbgzzSHRNQpbgM0sl\n0oZK3wtJkkRQFVsDVFlGNRIWyikWyikMVSFnaazUHaYLFq2Bzx+8sU6162HrCm4Qcmg8S60vHOrk\nPbpazqTj+XRdkQhvGwrltE7GVDm92UGRJSZzFs8dLFPrb3K51sfxRQCihIQsSaw1BsQxaIrEdN5m\npzPgpfNVXr3SIGdpvL3e5q8+McPJ6RybrYGg/d7D07+7jSRJ+O3vXWK+ZPPlYzdS2R9EGKrCiycm\n+NZ723jhybvm7lpKGRiaMCO4usi5XOtzpSYoZk8tFEYW7oWUzrEpYT4wvw8KZylt8LlDV5kClewb\n1hiAvh+OJvadQcD00EDgz89XqXV96n3hkLtSd3hmIY+ty+iqQt8NyVkamipzoJxisWyzXHM4Oplh\npeEwm7e4VOthaQpjGZOZvMX57Q6yBI4f4ocRqiTx9moLPxIZffWejyKL6zNvi3XpX57e5nK1j6ZI\n/JufmefdjTZ+GPPjKw2KKR1LV2j0fdquPwq4dodxLcXUvdkcve1ONEmSFWAF+Ownfzg/ffigSZZc\n8yAOo+SmPGlNkfnsgRJJkrDb9XhjtUnPDUlicbH23FBYpCrSKDkXBN3in7y+xqvLddKGQs8NWKpk\nmC1YRCTIawlb7QE9L6LvReQsYaZwaCLDN15Z4UpjwNvrHb5/bgdJlihYKk1TJUoSxtI6j8wWWK07\nxAm4QYhtqPzcsXFUReKx2fxtnVLuJVztShTHd97l6bgB7250iGM4PiWEzo2exx+9u0m149HzI0xN\nJmOqrNaF01vXjchbKpWsyQtHyqw3ByyVU0RxwmJss1oXwsYvHC6z3nREinKUsFhO0eh7VLIGlq7w\nynKdvhfihTGGqmDrChtNB02TKaR0SrbOZM7i4FgGRREGE6W0oLad3hQhZ4MgxAtEbsx41uCt1ZbQ\nJKV1nrjq/CmydIObzltrLZp9QYV8auHOchw+TPj5EDdiozngBxeqWLrCFw6P8eyS+Jwzpsbbay2q\nXY84goWyzZmtDnNFi3rPI2eqLJRThFHCWrPPTmePEhkTA7IMxycyGJqKoSv4YSQME3SZsayFbeiM\nZ0zSZo+ZgsVSKUVrEPLuepu5ks3hcUHXaQ8CTm90RlOU+WLh3j/HH8GBrNr1hvfxjRNsN4h4fbVB\nHIvP43pamK5K+FFM2lD5zFKR5arDruPhBxGvbLQJooSpvMV22yVjaMjygGcXyzy7WOTpxRKXqj2W\nq31ShooqSby/KZonHTfk0FiGU3N54iTh4k6P2aLNbtfD9SPe3+piD62z50s2ExmDjbZLs++z0Rpg\n6woDP2St6dDsiel034sop01ePDGBH8Z4ccKTC3niWOI3Xljk7HaXKEno+xF+JGhy57a7FFP6yIp7\nv7jnr5ePES9fqvP2epv/5pdO/lQViV87NcU/eX2d75+r8nMnJu7KMVi6wucPVUiS5LqN+a0XhDtt\ngn4UVNIGs0UbL4xYHLrCnd/p4ocx250BcZJg6SoFW+f1lRZBLNaJw2Np/r0vHuDsVoeOG/LuRpta\nz2c6b3GgnGa14bDZcllrDHhsJk/fE02ZnKUhy5LQFKZ1kiSh74e0BgFZUyWSJcYzBjsdl7NbHdab\nQnOeJOL570cJXhSjKBLzJZs4AVkSWu/Tmx0enyuMjFw+LsrwrfBR14w7bsNLkvTLwH8HjCHm2MI4\nKEk+caXrTscVo7uifd+J5G6HJ+by1Iabze5wDAsfWFrvwQ0iTm+2UWSZE1NZNEVmPGuStzQUSeK9\nzTbPLhVpOQFzpRQtx6PvRVyp91mqpLlU7fAXl+uc3eqQMTVmizZZW+fly3XmiikUWRnaIQYgQceN\neHejxbfP7gqeaRSRtwxeX2kxkTcppHQOKzKaIlFMG4IDm0CcgKIY/O0vLIkMkPNV/CjmqfniaBTq\nhZEQ296jdrcTOZMwjkkS9uVQ1B2IsFNDVegNA2qdIKLnRiQwNIbQ2GgNaDk+al3C1FUcX+dnj09w\naDzNSxdqbLYGzJdsEuDNlRbfPrvDd87sEMcJqiIxlrGYyIrcgIyh8vqVOut1BymBg2MpSrbOq1ea\nvHalRZwkzBdSlDI6M3mLS9UeuipzYio3mgo8ObTCvbDTZWU4zVOHLl7AKMD1Q/92Nxj+N7zNdwqc\nH1ovT+TMu+rQcz/hvc02a80BsgQnp3PXbDCdQGTnTOVNojihnBLXwGbToT3waToBY1mdzZaHH8XE\nxGQNjTCOyZsGvSAmZUrossxSOUXK0ChnDKI45kAlhaUr/OJj0+RtnSCKeeWyYDw7V10bfS+kPQjo\neyE9L+T0Zhs3iDkykblnBe9jWZMT0wlhlNzRvX652uNytY8sw7OLpRvMQOCD5sleJ/f8Tpdm3+fg\nWJo3V5usNx28IOLcjsZG06XW83h7o42hyjyzUMTxQ8ayBk/MFSikDQ5W0uwxbZaGG6OVWp/vn69y\nfruLGwojFD8MeXe9RdpQ0VWZlbpDo++jKhK7bZeZkkXa0MiZOnEiUUkbrNREkG3fiyhndLZaDsW0\nwZurfQ6OpTk0lmGuaPODizUqaR1Tlfn6k7O4YcRu22Wn5/KvnBzHCxJ0VUJVpI/8jF5rOJzb7pK3\ntRuMeR5E/Pb3LlHJGPzVJ2bu9qF8qnjuQImCrfHNd7buWvGzh+snEkvlNLqiYGryaOrzaR3H9QVC\nxtTImBrPLBU5WE7x1nqHjuOzlQgX1r3GxUZrwNvrLbwgIQgjFiopJhMTLxKTmUs1kfl3drtDztIo\npQwqGY2Fsj2UKqgcn8iw0/Vw/JA4hgu7XbpuyCvLTSxd4ZHpLI/P5RjPGqw1B/hhRNvxeWJujEY/\noDMIkGXQh3ufIIpG9MC+f2d7gv1ij2LtBkL/vt9g2/1wkH4L+FqSJGf29Rt+QnTcYERB8oKY41P3\nl6vQHoIoJk6SG8a8exc4CNekcCohusmDeL3p0OyLDeZu1xt1ITRFRpZEYJ6hKswULWpdH0mS8MN4\nVPy8vykuZstQOFCxSRkaCcKtZ7vjosgShiphaoLmUU6LPIrlWp8gSsiZCqfmciQxbLc8YhKiWHSN\n95KFD09mKKaFa1kUixTzN1fFBjxnaTyzWKLrBvz4iggCE3qRe8f69mrsN53YD2MuVcUiM5W3Rlbi\nlYzB4/M5Vmp9kgTqfRdLl+l7oriaL6WYLthUex6SBJd2e7hhxGzBopIxeWO1SbXnCXtcWcLUFE5M\n5vjXPzNHxw354YUaL19qsN1xyVoaS5U0J6dzvL3WYqvloSqgKMICNE4YGWOMZbwb9EyHxjOM50ws\nTYjZT07l2BkKsG+H41NZNlsuU3eokdoTfm633fvOIvNuYTxjsJHSUORr85Xag4BGz+PsdpdHp3NI\nMoSxoDB03ICmE6IqMgM/Ikli8qZKPqWT1lWcIKbleChBTMcNeXzOwjY13t/sEEYJpbTG+e0uEzmL\n8azJRE5Y7B+ZyNAeBCxVPpgATmRNbEMhY6pYuspW26Vg6+x03Hu2+AGYzN15g8MdUkTjWNzzqeuW\nL0WWOD6ZZRBEzBRsHD9ktS66ppdrfTRFZqftIstCBG1qMj1PhFWbqk7fCzk6mcXWFLpeyJNhTMPx\nR+v9asPh++d2uVTtc2IqixvFPDItOro7XQ/LF+Y3p2ZzVLserUHARtMhiBL8OMbWVRRZRlUk5ooW\nRyYzJFFC1tZoD0I0Reg9yylduEbaGpIkDTUJTeSheLrvh9T6PhISU3mLUzN5Gn0fQ5XR1Y9W/GwP\nNVgtJ7jBXv9BwzvrLX5wscZ/9pWj92wT8JOCpsh85ZFJ/tkbGzh+OLKFvxcgy9JNaWk/CRw/5ErN\nIW9rt32WhkM6uqkJx93xoRZcV2VmS2m6g4Dvnt+l2vaYzJu8td5kveGy1R7ghzGOH5E2VUxV4dB4\nGkmWcFyhJU5IMFVhnHVoPM1YWjTKDlYypEyF5iAgiGR2+h49T0yTbV3B0mQsTeH5A2V+tFzn3FYX\nRRb3va2rnNtuEcUJuqJQzhhMZE1ShjZ6RizeYbjrftF1QxxPNN92Ou4nWvzsfByFjyRJC8ArwBnA\nT5Lk5z7s+/c29kkiguLuR3TdgB+vNEmS5Lai8luNVgu2zqrsIEnSNQGSj8zk2O145G2htTg6kYUJ\n+NbpLd7daHNyWCxO5SwWyym0hsRCOc0vPDpFzwvZ7bicH1b5Ox1hb2ioMqdmRaHzo+U6QRSBpHJy\nOsdqY4CuyNS6Lo4kJjhzRZuOEyAlwtd+t+3xL97Z5vhUBkOTCaNk1A3suB9wW1tOcM8WP/tF3wuo\ndv1hkKGGKkvsdlze3RDTuplCCj9KuLDbRZVlSmmFnKWjyhLVnouyI/HOepvN9oB6VwjViymhtYmT\nBEWSGc8azBRSfOHIGBM5i6Yjcpf2nLfmijafP1jhqYUihqpwpdZnpmBzdDLDTMFmt+uy2R4MU+hv\nfutfbUZRTOlcrvV5a63F0cnsh479xzLmvlKdF0o2K3WHqfyNIvOHuDmOT4mchYypkrlqDdjLaUkS\nicu1PjlTZzxrkbeFfqvrCp1YFEfIskIEkEDaUjk6ZbJaH+BHCVEco6kSYRiTNpRRBpeli3v3ai3X\nbNFm9rrjk2WJ8azJcrWPH4qJT8cNmLuHC5/94uBYGkWWsDTlBlFv3wt57YoQLT8yk0NXZZRYIjXU\n8pRSOiensqw1B5AkHBrPkLc11psOl2t9FsspnlwojJynMobKlV5/OJkJubDTFbEFPR8JwQZ48fgE\ny7U+y8Mk+I32gIVSClNV0GSJ9sAnShISBKHn8HgaW1fIWiphDMcnc5ycyvL2epuNVoPZgs3lWg9N\nVZAlRnoIUdAKOu2lao+lcgpZgpQuNlqSJLHWHFDresyXbA6N75/mMle0OR8Ie337FuY/Dwr+3p9d\nIGuq/Nqzc3f7UO4KvvboFN94ZZXvnN3lFx59MINdu64o4tcbDo1+wGZrQMHWb2ls5YURr1xu4Ifx\nyPRgL7trDxlL48UTE3QGAd85t8tGw0WVJfKWMKwJ45gwFrk+03mbrz4ySdqQ+dGlBlEMS5UUzx8s\nkyQJf/TOJvWez4XdLl8+OkE5LcxU9rLgJrOmyHwkJmNqfPfcLhd2BPXZDSKOTGTI2zoTOZON5oDD\n45lrhhM3e0Z8nMhZGqW0Tt+L9t2shv0VPz+WJOl3gT8EvL0XkyT5p/v+rfCnSZL8G3fyjWlD5cn5\nAo4vnGvuR7QHAdFwBNh0AkppA8cPObPVxVBljk1mbysSL6UNPnewgiRxDa3AUJWbdlUdP6KU1gTX\nCnhsrkDPD/jBhTogcXG3S5zAn5+vkpBwcipHveujazILpRSllIkqC+ew1iAEEt5cbWGoMl99ZJK3\n1lv4UQ9LU/nC4QpNJ8BQZfp+RK3vkbU0ZEnimcUiUcxopDueMWhkBa3sQQq9q/d94bDVdfFCYSee\ntVSSZOgOIyWsDjcoU3nhqZ/SFdaaDm4/GlLsbIq2ykbTIU4S3l3vUEkbaIrEzxwdwwtiJnIWP3tc\nuOT0vZDxrMlux+WFQ2Wylsp8WViJf/HIGBwRx7bVHrBSF4XQ5w6VUSTpjjjmfT8aOeLsdNyPlfM8\nX0p97CncDzp0Vb6pxWqj71PrefT9kEPjafwwYrfrUrA0HpnOEUcRSBJtNySlqzhBSMcLiJoJT8wV\neWS6wHfP7lDOmCSxoGvt5VmMZw2OTubEQ+4O1t+uG440QI/O5B64wlZX5Vvy1ztuMKJ6tJyAsYyJ\nIks8u1jEj+JRh/+XHp+m6fhUMgbvrrcp2AanpjXmSyk0RabjBmRNjdWGw+Vqn6bj0XIC2oMQWYb1\n1oCcpXJ4PIM1DBw+PpXlpQs1KhmDQ+MZpgomZ7a6xEmM44mE+J8/OcnXTk2CJPPmapMzmx1sQ2W2\nYOGHMWNZAz+KeWwmx1bHw9KU0T1/bCLLhZ0eQSTCqbc7HmlTJUFQg6NYBO6CmOB8lOJnPGvuu3t7\nP+L1lQbfPrvLb7545JYW6w86nlksUskYfPPtzQey+NlrhMSxkDDIkqCEflgDv+eG+MOg+mbfv+Xz\n1lAV/Mgna2iUMoZw+8zm+d65GoYmM5bR+eyBMuW0QRznOTGdI2+tEMYxJyez/Pypaf7wjXW2Oy4d\nJ0CVRdi14gsmRsf1mSvZHBpL44UituDMdocTkznSpoKmSDx3cHyU63dsMsuR8cynTlNVZOkn0pLv\np/jJAg5w9aQmAT5K8fMzkiS9BPzTJEn+59t9c97Wyd/HzcPxrClyJeIP6GyrDYdmXzhvVTLGDYu+\nG0Qo8rX86TulEzh+SNsJqPd9SkNehqUrHBzLcm67hxvEhElCdxCSs3XShsJcKcXnD49RTunU+v7I\n8eTrT83w6pUmKmJT5AURr11pcLnqEEaCmmBpClJKotr1iJKYqZxJMWVwYCx9g+e7qsg3Dfe63xFE\nwhXOD2NURcbxIxbKNl4YoytCaFywdYopIS6cyJi4YURaVymnTNKmyl97cobf+YsVxtImXpQwXbAw\ndRlFSnNkIsfh8cw1n2cCHKikMVWF8ZxB2wl4d71Ds/jBqLnnhpze6IyO8Zr33ybhPmuqjGdNOm6w\nL4ebh/h00XB8HpnO4/ghXzgyxnKtx07Hw49FAO18JUMQxSypCkEcU+v6tF1hOX9kIk3fjzk4nqXp\n+Gx3XAZBzCNTonAxVJkkuXOx71I5xcpQy/WgFT63w1jGpJr1CKJ4ZFkNYiJmyh90e/ecHEHQRS/u\n9gijmJShcHqjg6ZJfOmIMIvpeyHntnsMwhBDUfDCGFOVmcxZLNf6Ixre0wtFnl0sstEaYGgKC8UU\n+ZSOJgsa4mTeYKmSJmeL50GcCGpkGMej+zxtirDalK7QcHxyljZqkhTTBr/+3MLonK43B8wXU6IZ\np8oossRC2Wa77bFQfrhW3ApJkvBb/985ymmDv/n8wt0+nLsGRZb46iOTfOPV1ZFL6IOEIIqJh/m/\nU3lBGU4b6ofq4Qq2zmTeFGv2bah3YxmD8ZwwI1kqp8S9HCWc3ekynbc4u9Xlc4cMZFliMmvywuEK\nP15pEiaSYKMoMqaq0CQgTIQT6HrLod73sTSVk1M5vnR0jLfX26w1HMazBilToZjWeXqheANV837U\n591x8ZMkyd/8mH7nFnAYMT36fyVJ+naSJCPLbEmS/hbwtwDm5h6MkbCmyJyazV/zWnGY/CuSs689\nDTsdl/c22iiymJzslxMrIXir03mLqaumK/NFm688MoEXJiwWbX5wqUbXDVmqpPn8ocqouEqbKpCg\nyjJ/6dgYT84VeflSjZVan3zaIEkSCmmN0xsOrUHIq1ca/PXPzrPV8Viu9sEUdqgr9T6ltE7hUxQO\n3g1st10sTWG2aDGRE8VCxtCYyFpMDav2PXG/LEvoikwhpbFSDzgxnSOlq1QyBnXHp+H4jOUMprIW\nXz01SQJstgZstV0kSXCC24OA3LCrv9Ea8NRCAUNV+OHFmvhdDYfNlsiAWbqKbytLoiP19nqLcJjO\nLssSj8/mbyrulCTpgSxUHzQcKKdZbzocHEuTszTGh3SFoq1jawqWLopxTZK4VO9zoJxmsiAaFKWU\nznanS9vxBddBgpytDmlyCZam7IuaOlu072l9zycJRZZ4dCZ/+2+8Crau8qvPzJEkCd86vc37Wx10\nVebpuQJuECFJcHwySxgn6IrEdnfAWmNAox9wciqHF8bIMmiqxCMzeWaLNqYmrMh/8bEpFCnhe+dr\n9L2YzbZYE6pdD0sTE6yUoZIyNR6ZufYcX01hDaOYt9dbuEHMyakcOVvj+FSW5WqfWs/n9GaH45NZ\nDo5lRkGdbSdAVaQHWrfzUfDnF2q8stzg7/zlE/eU1uVu4GunJvm/Xr7Cn76/wy8/YKYPeVvnyESG\nQRCxUEqhqzLntrvsdFwWy6mbrpGyLHFi6s6et5oiX+PAeqXeJ0rEelLJmJia2MtVuy7fPVslZShM\n500Ktk6SCGv9lVqPN1Zb5G2dV6406LgBXhjz9EKBw0NK27NLRR6fyzOVt1CGEpQHpam1H7e3w8Bv\nA+NJkpyUJOlR4C8nSfJf7+cXJkniMaTNSZL0R8BJrsoLSpLkHwD/AOCpp576CEak9wfGsibPW9oN\n0x2ApuMzCCJUWaI7TAaO4oTxrHFHF56lKzwxV6DrhkzmTNpOQBjHlNIGByof0BF+5sgYnz9UuaGK\nVxX5mrTpvhfRHoRYhsaxiSxBFPGDizUkJIIoZrXu8N5mhyhJsA2FIIx5ZbmOLMlstV2eXSxSTOl3\nFKB5v2F3WKgCt3S1avSFT//jcwUqGYM4SfjWezuiGJIkLENhoZTinfUmqiTj+CGHJtJkLQ1bV7ky\ndGRarvbZ6bg4XkTW0pgr2uQtjTgR18xiJSXcnWSJek9MFRMkHpvL44ViInep2sfxIup9nyRJKKcN\nql1P6BNk6a7lLvw0oz0I6LoBE9kbc2FuBzcQlKarRbqTOYtffXqWcztdzm93OLvdY75k8+ZaCwmJ\n5sDDNmWqPQ8/DPFCQc0Yy5goinACbPZ9jk1mUBWZiZwQxjp+SNpQH5iH391EEMWc2+6SJDBbtMjb\numAI9H3ShsqFap/2UPjfc8PRFP31lQZ5yxgGDOdwQ2FisLeRzts6jh/y+koTU5V5fK7I+d0+BUsj\njhPag4C311qAcLXcc338sPPbcPyR2c5GS1jv27owzdhquzR6/rD4zuAGEbWex9mtLrIMTy0U74tQ\n608DcZzw33/rLNN5i1995pNUQ9wfeHy2wHTe4o/e2Xrgih/gmr1AGMWsNYTxyUrd+dgbRFdqffKW\nxkzB4shEBj+M+YPX11mudQkjyNkaj88VGM+aTOZE/uC/+swcGVNjtTlgqyUcKMcyBsensuRMjW+9\nv005ZfD8wRJuEJHSH6y1fz+th/8N+E3g7wMkSfKOJEnfAPZV/EiSlEmSpDv88nngf9nP+x8k3Mrl\nRUok3ltv03YDtloOfgSL5RRHJ7J37EQiqII6zb7P6ytNgJHwHWC361Lr+swWrWuOI44Tqj0RmNfz\nQiQkQZ9QZGxDGYYqahwdzxHHwmL38fk8bSdAkkSxpqsSTUcIfC/Xumy3XTRF4tefW/hU7SM/biRJ\nwpW6QxDFwjziDjeqZ7c6OH5Ey/HpuRbTBZsXDpf5k3e3WG04PHegzIVqj7XWgJiYOJbwwphvnd7h\n2cUCxZRwgUqbKu7QWvj8doeXL9YYBMIZbiInrK+fXigSRDHvb3ZIEHSlq+mSlbTBWtOhktZHdBVZ\nlnj5Yh1FkXhmofiwW/spwg0iXl8R3PCWc2MuzIehPQh4faVBknCDc2LG0tAVmUs1h9WGgxdGLFVS\nnNvq0HACvDBBkqDjBKOJpGWoPHegxImpHBd3u2y1XeaLKZIk4dXlBn0vZCpv3XeOm3GcsNkWqe13\n22BlreGw3XFJkoSWE3Buu0sla/D4bIHD4xniRDg9ZUyV1680eO1Kg7yt44XCxS2KEyxdYSpvkTE1\nsjfZjFypCUq1H8XkLI0vHK7QuYnrkqkpI63pj6806LrhTa3nc5aGpQsXuLGscc3riiIRxwk5S+e9\njTbbbZdBEGFpCnEsbPIfFj8Cf/zeFu9tdPgffuXUwyYTYtLxC49O8g9/sEyj7480JA8iVEWmkhGN\nxutdVq9Go++z3XaZzJkjQ5Wt9oBz28II5GY6yp4Xivd1XQ4YaYq2zj9+dZVLu30SEsayJhlT4+R0\nbmSm4vghpzc7pC2VgqfhBQYgcXwqx1whxffOVzm/0yVjqGiKRNsNkJH42RPjD8y1u59djp0kyavX\nffAfxcD785Ik/VeI6c9LSZK88hF+xgMNJxDWtGGUcKU+IGNqDPyIKLnzQVgcJ/hRjB/Fo9e8oZgu\njGLeXW+TJEKk++xikddXm7SdAEWWqPc8lmt95kspCrbOyeksT84XhkLWNMtVh4WKzVcenaBoaSBL\nI5eSMI7JqDpPzudZLKe4tNtjtTEgjBP6XnhfFz/Vrsel3R4gKGQHxzIUUjrjWYO0qY6Ev0EYoakK\n3UHAOxttLlf7FFMa1Z5Hztau8tvXUWSZ1iDA0BS6gxBVlkmnlaGHv4ulyZQzBsenMkxkLZqOz2bL\nHVqUe8RxwnbHpZg2RtlQN6NZ7iFna3zxcAX4YHx9blv0IqJICB8fFj+fHpLkg1yYvfO3d+/erDkS\nDClIBUvD0BR22h6b7QEDP+Krj06Ozml7ELDZHuAFEboqk7U0HpvJIwEtp057EDCWM1goC+OJluOz\nULSp9cRE8GoKUxDF9Ie5Va2B/8l/KB8zrk5tf3qheIOD0qcBN4jQZInzO2LaMwjEve5HMZaq0HJ8\njkxkeHL+g4BgQ5Up2DrdYaZHOS2cohZKKR6ZybHVHnBmmNv2xFxhVMgUUhqbrQE7wyKLRBQq7YH4\nOadm8/S9cNR9juJklM/VcgJ2uy5dN2S2YKOrMoaq8PzB8g1hgmlD5fkD5VHBdnpTTMB1Rdjg6qrE\nWObOi01nmAfyINLB3CDiv/2TsxydyPBLj0/f7cO5Z/DLT8zw9//8Mn/w+jr/zgtLd/twPlGcms3f\nNpDznSElvdrz+MLwOb3eHBBGCcvD6U4Qi+iQSkbEioRhTMaUZ00uAAAgAElEQVRQmclb1Pser682\nqfU8Om7AdMHiS0fHODqRIXWVy1zbCTi71cUfWmu/eGKChIRHp/PkbI0fXa6PXCEbjs+Vmmj6ZgyV\nF45UHogJ0H5WmZokSQcYRuBKkvR1hH5nX0iS5I+BP97v+36acHAszenNDoYmkzE0Klmdo+PZOxad\nx3HCa8NO3lzR4sBYmiiO8YKYPz9fZaEkHmpeEGOoMu+st/ne2SqllKBMvLLcoOX4HBof8KWj47Sd\ngN/78SqtQUB7EPArT82iSNI1lo3PHSgRRAm1nsd6czCigC1VUvzwYp1y2rijvJh7GYaqjGzX97of\nb6+1aDkBXTdkpmDzu6+tsdEckDaE/etOR3RxZvJZ5oaby0EQEkQJ1Z7PTMHiM0slOm5Azw2o9Xye\nWiyw2/HIWSEbzQFtJ6DtBBRsg1Ja/Ds0niYcOixFScwbK01Sw43R7Ywxrl+45ks2gyBCV67Nj3mI\nTx6WrvDoTJ6OGzBTsEiShB+vNOkMgpsmY3//XJW31lpIEvy1p2ZAEvbkiiymhXsFk6HK+GFMve+z\n23GxDZm3N1p87+wuHTdkKm/w735uiamCRdbUeXezhevHJEkipD9XXSKaInN4PEO1547yq+4nJFc1\njZIPSXD/OPHeRptG3+fweIam47PRHJC3NXKWRssJWCynWSynWCjbOH7EUvlaY5iz2x1kWSZna5ia\nzGzRwtYUel7EdtslbfZpOj5xLDYyPS8cRSBM5iwKts6l3S4/vFRnt+OJ9+sqfhSjBJHIJOt7PD4r\niqYjExl2uy6VjME7a6KIcbxoVGRFcXJT04ur15qDY2nWGgOm89a+s1IafZ83VwVD4bHZD4+DuB/x\nf/xwmfXmgH/0G8/e1tn1pwmi4C/wj19d5Tc+v/hAbKo/DLczBjA1hV4UYl51X03lLdYaDtWux7dO\nb1NKC1ZPJWOw2/FoDwLiOKbuBCyUbFw/RtcU5ss2j07nieKE75zZZavjMley+dLRMYopHdtQqNY9\nxjMmk3mLpUpq1Hj42ePjFFZ0Zos2M3mLnY5Hvxvy/naHsZzJscn7a/p/M+yn+Pn3EVqco5IkbQDL\nwB3ZVT/EnaHrismLLEk8u1QcuvDsv2Dwo3jUyav3Az57IEsYxXzvXBWA1caApxeKdNyAUsrg++d2\nKaQ02m7AobEU72628QKZgR8znjWRZdhsucQJvLfe4q88Nk3L8ZkuWKMiQFVkVOVGwXPBNu6alaUb\nRCNv/eszOW6HthNgaPI13fecrfH0YpEgjEcP5z23JS+M6bsB220XP4xZdXymciYJwmVtriSEyKrq\nQJKw3fY4Npnl8TnxoC9HxkhzkzU1Wv2A1HCzskcHiK/axJmawtdOTfHeRlvw+zWFhhNQ63m3LTI3\nWwM2WwNmCjYTQ/7vY7eYFD3EJ49KxhjlqfhhTGcg9BX1vgdcW/yEcUycxARhQpIkPDKd4/Rme3Qe\nHT/k7HYXS1Oo9zxUWWI6bzKWNgmCBFNT0VWF2UKKpxZLo43Yk3NFttoDyhnjpg/ouZL9sYf/fVpY\nqqRFXpGmfCqT54EvChSAlXqf8KpcsxcOlQkTYSQhSRLHJm9Oc9zpuOQtjcxklq4XkjE1CmkDc0h7\n9cKImYJF1w3JmioZQ6Xnhex0XMYyBqos0/civCDm1EyOKElYKNvMl1K8udoiSaDZD3DDCFtXR+u2\nG0Rc3O0Rx8LAYafjjtwihT7p1tfATMH+SHkbIFwp95a3nhc+UMXPbtflf/3uJb58bJznD5bv9uHc\nc/i1Z+b4T3//bX50ucFnD5Tu9uF8YnADcT9+2OT5xFSWc9vda+6z6bzF0wtFzm13ObvV4eJuj+mC\nPVq7VxsOxyaEPtPSFDKmzBMzecI4oZI16AxCLlZ7XKr22Om4HKikODaZ4+lhHmA5rZMy1GsmrouV\nNItXxSr84mNTfPvMLsWUPtpb3u/Yj9vbZeDLkiSlAPkq3c5DfAzYbgvhvCyLXBhZktBU+SMVP6am\nsFBOUe95LA0v4Os5p3uOQAAL5RSKIjOdt5gtWrQHAd89W+XEdIYwTlgqp3l0Js9ux+WzS2XeWmuK\njuMguKnPetcNOLMlNmAnprJ3zQbx9GaHZt9Hlvt87mDljq3CL1V7LFf7qIrEZ5ZK1xRA1/PX9xzX\nxrMGOVvnkeksV+oOJzNZTE1BliRmihbljMnLF2s4foQiw8npHPW+SGDPWRraVSYT7eHmt5Q2qGR0\nJCQm89ZN6WgHKmlajo+mSJTT+h3xps9ud4hj6HmdD+Uf72GPH6wrMiemsvsW5T/EnUNXZZYqKapd\nj8XKjVOWzx+ssFzto6kStV5A0xF29qoszslbqy0u7vYwNYWdjoepyfihuN6SBKIkxg9jfv7k5DUd\naEtXRmsFiCLsvc02SZJwYip3X6fQK7L0qWZKmZpMIaWPdDd7m4Vjk1l0TeF2d+jAD9lpe1S7Hoos\nUckYOH7EozM5Gn0fNxCaQ12Vr3Fle3O1iRfEbLYGVDIGHVcYGCQI44G9wmSuaHN+p0sxpWNdd15N\nTeHJ+SI9L2Qia1Ltuux2XSQkjkzcmDH1cWEqb9LzQhJuPmG6n/E//cvzeGHEf/7VY3f7UO5JfPXR\nSf7ON0/zjVdXH9jixw0i/uJynShKODCWvkF/t4eLuz1aTkDHbZOztNG6O5238MKYrhvghjGaLCHm\n2AlPzhdE7MVYepSvtdtx2WoPODiWYb054PRWGxlpuBaJdf/QeIaOGw6Nj3QuVXvsdjwOVFKMXRe9\nMpGzeOFwhXrfZ/E+nP7fDPtxe/tPrvsaoA28niTJWx/zcf3UoeeJDW8ci8mNOUzY/qg4eJOMnVtx\nTpcqaZYqaQZDUX6SSKQNlZ224P8risx//OVDNPo+q3WH71+ocmgsPepWX4+VukNnENAZBEzmzbsm\nMt7b3ElI7GeavhfsGUaJ4NvGwint9GYHTZE5Of3BZjBna9d0cl48OXnLn7s30lcVGVOT2Wq5o99z\nNb0pZ2mcms3TGQSsNh2iSLjo3ezztnSFzx2qkCTJHVMGcpbYmN1pF3ytIah3ALWef0cF00N8dOzd\nj1ej7QRcrIqGwqHxDEmSiE2pBIokI8uiYNloDdjtehiqRCltYGoKLxwp88RckWTY/a92fYIk+dBr\nZnvo4gWw1XZv+bB+iBshSRJPzhfwgoiXLtTQFRlbV+7oM/TCiJcv1VlrOoxnTLwwousG5GzRIMnb\nOoYq37SRo+ydywR2Ox6Xaz0WSik+d6h8TVd3Imd+6D2cs7QRhS6IEjRFJo7vfH35KFAV+b4z0rgT\nnN5s87s/XuPffn7x4T10C5iawi8/McM/emWF3e6xawr6BwVuEI2C7vteSBjFXKn30RT5msbM1XsW\n+ar7TZYlDo6lkSRYrvZZbThoioSqynzxyNgN5kv1vk+169N1W4xlDA6U0mQNlZylj/YRaUPlhUNi\nEhlEiYgpQRRg1xc/8ODFGOyH9vbU8N83h19/FXgN+NuSJP1+kiS/9XEf3E8T5oop3CBGU2RmC5YI\nKE1//BSNW01hel7Iq8t1oS8Z+EzlLdKmytEJ8UDaCyH7gzfWkYDN5uCW9pTltMFOx0VX5ZG7yN3A\niaks2213tHG4UxwaF4uMoSqs1PskCbx0vkp7EGKoMhlT5cjE/h/Uj8/lqXY9SmmdKE5G+qGbccAr\nGQNTk1keCrVvN2rez8bk8dk8ThBh32E3v5jSWW86KLJE1nrwxMj3Ay7sdmk5AU0C5oo2Z7eFm5+m\nyBye+CA3ojJM/DY1hc4gIGuqWJo4Z5Ik0fcEbWrgR8QJ3CpwPJ/SPhDQ3wWDgAcBuiqTt4XG505p\nXG4QC3pZwUaWYCyr0+gHqLLMZmvA5WofRRa06OuNAR6fK1DreYRRwqVqj5m8zUzB+okMBDRFHuW0\n7WcNfQihNfu733yfvKXxH/6lQ3f7cO5p/PpzC/zOX1zhd16+wm++ePRuH87Hjryts1hJ0fdCliop\nrtQdrtSE9bWlK6OC79hkloLtkrW0mzY4DlTSTGRNFBneWG0hSxJPzHpMXjct/cDAxKfjBhi6zHTB\n5rG5a11B9/YNmiIauW0noLwPk5L7GftZFUvAE0mS9AAkSfovgd8HXgBeBx4WPz8BdFW+xmLU/pSL\nBscPiWMRcHrQSKMpMkcmstdoZUxVwdZU4hgOjGVu+TCcyJkUUzqKLKHI0shq1lBvPr34pKAp8kfq\nVNi6yqMzefwg4p31Furw71BkCT+KSX3EzYSpKdccz5PzBQZBxMRNuiwgCs4DY2k6g4ADYx8f5USW\npX0VpZWMwecPVZAlHlLe7hIkSbgNTheEMLXrCecugPHcB/lATy0UaQ8CTE3mraG242rK2tHJLKsN\nh7GM8aHC66yp8blhV/DhpvejYTQB+v/Ze/MoubL8rvNz33vxXuwRGRm5KiWl1pJqU1XX0tXVi9vu\nbmMDBntswAvbYLsBe3yM4RymMXOAYYA5MAxgBsbGbmMONgYbMMaebrtx293VXa7uqlKtUpVKWyqV\ne0Zm7BFvf+/OHy8yKlXaMqVMKTP1Pv8oM7a8inffvfe3fX/+jZX7bkQhleDwUIahnMHR4SwXltso\nIor0mD3DNQgllhuQUKPocSEVRZ9TerS+dByf6WoXI6HcddQ9kkGPft6LHvnt5L+9Mc/LV2r8o+95\nrB9Ji7kxh8oZvuORUX75G1f5q588el+dprfDD0IWmza5pLapGsIj66L5xjrDRl+3vm7kzJIxNCbL\nGSptl4yhYvvhda95aDTHldUuhaTGfMPG9gIOD2dueg8LIXh6k2vVbmczM+wAveakPTxgUkppCSGc\nm7wnZpcwlDU4OJjG8UOODmdveAMkNIUf+vABVjoOB3o36FIzkl3eV0xdE1Va77VYLzX79OTArpG7\nnq6ZpHUVywv49MkRltsOpbTOxBaFfotpndvJDGw2VcJ0fZZbDuWs3o/WbQUbrZeK2XpM16dheuRS\nGgPpBMtth1xSQ1MiQZH1nv2sofUPDk9PlqLeLOs2vFJmY3VhEBs9W4EQYtOHifUpj4/tK1Dtuv3D\nsyTyFA9mDd6abbDSdlAU+OjRcl98JmtoPH90kDDkGkXOWxGGUYpvMqFcl/ISGz2bp2G6/MMvnOPJ\nA0W+/5m4oelG+OwnDvM7Z5f4T6/M8CMf37my1+8tRb0LFQWeP1K+I2NhfykSQNJV5Y6k99cyhaSE\niYHra+QKqURfxGh/KY3thzc1KJdbkUjTvmLqgTF8YHPGz68CLwsh/nvv9+8C/mNPAOHdLR9ZzD1F\nCNEvlrsRUkapFF4gOTqcRVMVqh2Hs/ORLKofylsc1NdJzd4bpdlNYbo+UytdCqnENV6XUEoMTcXQ\nVIoZnQO7oNDvzdkGphMwU1P6fQJidj7LLZuVtsP+gfRNN8OkprLacVluRb6mk+P5WxaHRwfm2OO8\nm9FUhZF1xshjE+9nB9xsKa20bCpth4mB1IaNn6nVTj8N5+lJZdc4qHYq//h3z9OwPH75ux+7b4I/\nu40nDwzw4UMlPv/1K/zZ5w7u2IP42hlmfY+2O+FusmBURWxYblpTFbI9R1bH8Zle7VJMJ5gYSFPr\nupyZi85wbhBeE53a62xG7e3/EEL8DvDR3kN/RUp5uvfzD235yGJ2FMsth+lVkzCUJFTRVya7HVJK\nJksZdFXF6Kkg7TQuLHdYbTssNW0GMnrfQ3J0KEsqoZLS1V3TpVywVjC5MXw/RIujOvcVPwg5O/9+\n0+Hnj1wrh5vWNU7tL9KxfVQFzi9FjXa380gVhPKmaXG3ei7m3nFyLMdCKkExlehHfVw/5MzaXLI8\nnv+AtHLYqzW8vkYwvp5bxavTNf7jKzP88McO7UkRh+3kJz91jB/8/Mv8yjev3nH053aNRO+WE2M5\nskktqqfcoHNhp3B+qU2967LUtBnMGNfc9bf7xtZ6wO0VY35Dxo8QQgXeklI+Cpy+3etj9h4pXWW5\nZbPYtBEKHB3OMZg1eHyigNtLe/sgjh9werqO4wc8tq94T+t9NkNaX+tVJEisqwDXPqDEsht4Yn+R\nStu+bYF1EEq++PYCFyodTo7l+Y5HRvfMorbbUJUoNcpyg5sWp5ezRr9+Q1MVpGTbmga/MVOn2nGZ\nLGeuU4y8VOkwvdqlnDPi3lD3GUN7X0FOSskbsw2qHYeW5VFI6dcdzOpdlzdnG2iq4OmDpWueP1zO\nYGhRX7M46nPndB2fv/Hrb7G/lOKnPnP8fg9n1/H80TKfOD7Ev/rKJf7U0/s3VSvlBSGvTtew3IBH\n9xWuiZhuJQlV2bXKfWldpd6NShg0VTCg6zy+v4Drh4wXbr6fdByf167WkVLyoYMDu8YZfCs2ZPxI\nKQMhxFtCiANSypntHlTMzqOQSjBaSJI1NAQCL4iU6W4kibhG0/Kwek351rqH70SODWcpZyOVrDUP\n6m4lpasbMti6rs9c3UJKmK2bWF5wwz5CMduPEIJnJku0ba+vrHUrxm6xSd0tXhBS7UlcL7fs64yf\nteadq20HPwhjAYwdghdIah0XgaCcTfLovvx1RsxKxyEIJUEoqZsuKf39eaQoYk/J2N4v/uEXzzFb\nN/m1z35kRxft72T+5h95iD/+/7zIz371Mp/7zo0rv7Vtvy8Kstyyt8342c2cGM0xnDPIGFq/pnMj\nNX21jovXE1aodtwHx/jpMQa8I4R4BeiuPSil/BNbPqp1BKHkjZk6Hcfn0X2F+9YzJiZSippa6TCc\nS26oGLqU1hnI6DhewERx526sQojbFoGfW2yx1LI5NJhhcpd6fdaTMzROjuc5t9jm4dF8P/oVc3/Q\nNWVHdLVfUxuqtG0ODr5/z87VTS5WOgShRNcEo4VUbPjsIHRNYaKUYqUdNba+0VwaL6ZY7Tjo6t2r\nwN0KLwh5Y6aB5QU8vq+wI1Odt4MvvbPEr748w2c/cZhnD5Xu93B2LY/uK/C9H5rgF1+c4ns/tO+W\ntcjrKaYSDGZ1TDdg/8DOPW/cCzqOz5szDRQR1VKtRXmFEHe0zwznDRabFhJuqk6729iM8fO/b9so\nbkHT8mj0GiwuNKzY+LmP7CumNtV9W1MVnjo4sI0jujcEoWS+bgFRlGQvGD9CCD750DCffGj4fg8l\nZofx0Gjumqa7AHN1q9+k79lDgzu2GPlB5sRonhOjN38+a2jX1ZNtB3XTpWX19uym9UAYP5cqHf7G\nr7/FqYkCfz1Od7trfvqPnuAP3lvmb/3GGX79L39kQynZiiJ48sDuP29sBZVWJG8NUYuEA4N3Zwwm\nEyofPjy4FUPbMWzYdSelfAGYBhK9n18FXt+mcfXJJzXyqajhXtxZfncgd6Kk212wNvcUhU0ZfzuF\nvXY9Yu4N6+dNJGUP5ZxxTY+KmPeJ77OIYkonm9RQVbFnvMS3ot51+ewvn8bQFH72zz4VOwa2gMGs\nwU//0ZOcvlrn3/7hlfs9nB3NjdadoZyB3qvhK+f2vvPhTthw5EcI8aPAZ4EScATYB/wc8KntGVqE\npipxCHkX4AUhDdPlcqWL6fk8PFbYccZqw3RJJtQ72pyiBrSF275uJ+EHIaev1jFdn0fGt68ANObu\n6DhRN+6dVCNQadmcXWiS1jWeOjjA/lI6rgm5BWs9d44MZ3d8MfTdrIMbQdcUnttjXuKb0XF8/uIv\nvcJc3eLf/6Vnt02E5EHk+56a4H+8u8w//t33eGayxKlYYOU6Km2bs/NNUgmNpycH+uUIuWSCJw8U\nURVxUxGdB53NuPB+nEjmugUgpbwIxDkzMYSh5NUrNf7wUpV3FpuEISy17Ps9rGu4VOlwerrON6eq\nOH5wv4dzT+g4Ph3bj65Hc2ddj5iIasfh5akqL09VWe3snF7RSy2bMISO7dO2/fs9nB2N64estKNr\nt9iw7vNobs2DuA5uF7Wuy5/7xZc5u9DiX//ghx4Yg+9eIYTg//q+xxnOJfmx//A6lXa8h32Q5aZD\nGEYqg2upphDt9y9P1fjmVJXmusdj3mczxo8jpXTXfhFCaNy8z1rMFlFp2Tv+4BpIieUFpHWVhKJE\nxbc36Dp8t3Qcn9maeUeb9pp33Q8kbk+1ZK+TTyYoZXV0TWHfNlyPmM3R7c3ftVzs6LGg3yyv6+wc\nI2NfMdXvy7UZudndSNP0mK2Z+MGdrQu6pjBeTJHQlB0fHeuuWwedB2Qd3A7OLbb43p99iXd7hs9n\nHh6530PakxTTOv/vD32IWtflh//d6S1fI4NQMlc3aZju7V+8A9k38P46vV7dce28E4ZRE/eY69lM\nPOwFIcRPAykhxGeAHwN+e3uGFQORXONa991Ayh1bb5JQFU6O5VlpOzx7qLQtfSLCUHJ6uoYfSJZb\nNk9Pbi4V8thwFkVEBkFuD8g0bgRFEXwoLgDdEUgpOX21jueHLDSsfvHovoEUXddHyp1VTzaYNfj4\nsaH7PYxtx/YCXpupEYaRuE6U3rp5dkszy6PDWURvHdwLcrX3GtsL+KU/nOaf/94F8qkE/+FHPrzp\nvShmc5zaX+Rf/eCT/Oi/P83//Euv8vm/+PSWzd0Ly23m6xZCwEeODO66FLFSRr/hOn2glMbxAzRF\nYWQDUtYPIpu50p8Dfhg4A/xl4ItSyl/YllHFABCuK2QLw50dZBsvprY131kSecch8tZsloyh8fhE\nnDMcc/9Yu5/XT19VEZwc2x0H573IWtQNrl1v9yrxOnhndByf//bGPD/31cvMNyy+/eER/s//6bEd\nIU//IPCpkyP8zPc/yU/92pv8wM9/k1/4809vyXlj7Z6X8tp1ebejawqPjO+uGuV7zWaMn5+QUv4M\n0Dd4hBA/2XssZhsYzSfxA4mUbCqN7OJym5mayb6BFCdG98bBSlUETx4oUu26jO0wIYWN8vZcg9WO\nw5Gh7IYakcbsHYSIonCrHWdXzF/bC3j9ah03CHlif3Fbork7gZSucmp/kZblMbHLeoNcrXa5vNKh\nnDVig2aLkVIyW7N4+UqVr55f4avnK3TdgMcnCvyT73ucjx7dfsnwmGv5rlPjZA2N/+VXX+eP/cuv\n8y++/0m+5fjdRaePj+RIJVSySW1HCc7cS8JQ8vpMnZbt7Uihqu1iM1f7LwAfNHT+4g0ei9kihLiz\nrttzDQspYb5u7RnjB6L83916CHP9kEorKoqer1ux8fMAUkgldk39TN10Md21bunOrr3vNkI5a+zK\n/nHzDYswhErLwfEDDC2WWL4dUkpalk+161DtulQ7LrWuS7UT/V7ruiw1bc4ttmj36iaGcwbfdWqc\nP/PMfp7YX0SI2/ecidkevvXEML/1Ex/jx37ldf7l71/kE8fKd3U9EqrC4aHsFo5w99F2/Pd7aTat\n2PhZQwjxA8APAoeEEL+17qkcUNuugcXcOfsH0szWzR1VQ/Cgo2sKo4UkKx1nxxdFx8SUMlGvFi8I\nH5jNcLexfyDNpZUOQ1njgTd8qh2H5ZbDSseh0rJZ6TistB2qHTcydHpGTq3r4t8kvymX1BjM6Azl\nDL77yX08PJ7n8YkCD4/lY4NnB3FkKMtv/vhHaTtefF22gJyhUcrqUfT7ATozbiTy8xKwCJSB/3vd\n423g7e0YVMzdcXQ4y9Hhrfdm2F5A1/EpZfRrFp2u4+MF4Z72Dm+EWtcllVBJ6Tc+iHywmDoIJQ3T\nJZdMoMeNIzdMGErqe/R72yn3kqGpsXTvDqRhuiRUhYyhbWnvpbbtEUp2TWTyg/yVX3mNV6fr1zyW\n0VXKOYNSRmdiIM2piSKDWZ1SRmcwqzOYiZ4rZw0GMokH3oDcTaT0m++zMbdn/VnudsJITdNDVcWe\nSwu87f9GSnkVuAp8RAhxEDgmpfyyECIFpIiMoJg9jheEvHylhueH7BtI9Yu027bHq9ORWtJDo7kH\nNqpxqdJmetVEVQUfOTy4oQaCZ+abrLYdUrrK80cGYy/WBjkz32Sl7ZBMRN+bouyN7y2+l2JuxWzN\n5PxSG0WBZyZLW6ZaWe+6vD5TR8rIQbMbI30//q1HsdyAoZzBUC5KY8zsscNaTMxWcLOz3I1YbFq8\nM99CCHj6YIlCenc6R27EhlcHIcSPAp8FSsARYAL4OeBT2zO0mJ2EH0i8Xl+ItVoAAMsLCHvtItY/\n/qDRdaL/exBI3CDckPFj9nLKbS8glKDujTP8trM2zxw/IJQShb3xxdleGN9LMTdlbU6EYbTubpXx\nY3pBX/Fut/YE+eRDcb/1mJiNcLOz3I1YO9dIGa05BR5A4wf4ceBZ4GUAKeVFIUS84jwgpHSVE2M5\nGqbHZPn9Yv2hrMGhoQyOFzJZfnA91cdHcqiK2FT/jIfH88zVLYZyBuoeiV7cCx4eyzNbNylnDTR1\n76S9lbN6fC/F3JTJcpoglBgJhaEtFGgYyycxHZ9ASg7E0caYmD3N+rPcofKthZcODqbxgpCEKhjJ\n7z5RmFsh5AZ7GwghXpZSflgI8YaU8kkhhAa8LqV8fLsGVy6X5eTk5HZ9fMw2IwHLDZBIUgkV5Q7T\nuqanp4nnwd7CC0JcPyShKpuq24nnwu7B9kKCMMRIqGhbbNzH82Dv4YcSxwtQFYVkYmNrwoMyD9Y8\n7yBJJTTiDOlreVDmQcytee2116SUckOLx2YiPy8IIX4aSAkhPgP8GPDbdzLAjTI5Ocnp06e380/E\nbCNr+aIABwbTHB/J3dHnPP300/E82GO8cGGlH3r/1MnhDdc7xXNhd9B1fL5xuQpAMZ3g6cnSln5+\nPA/2Hq9O12j2JHefPzpIWr/98eRBmQdr9V4Ah4YyHHnA5Zk/yIMyD2JujRDi9Y2+djM5I58DVoAz\nwF8Gvgj8b5sbWsyDRCGVQFMFihJJ58bErFHORvOhlNVjoYc9SDKh9gvOB3dhD52Ye89ar6VsUouV\n1z5AMZ1A7e2lg/FeGrNFvDXb4Ge+fJF/88JlZmvm/R7OPWXDkR8pZSiE+E3gN6WUK9s4ppg9QlrX\n+NjRMpKomVhMzBqPjBc4MpTF2GNS1TERqiL48KESXmPN2I8AACAASURBVBjGB9mYDXGonGG8mCSh\nKHtGwXGryCUTfDzeS2O2CNsL+Lv//R1+7fRs/7F/+j/O8/f/5KP8wLMH7uPI7h23vYtExN8TQqwC\n54HzQogVIcTf2cB7x4UQrwsh7F6NEEKIfy6E+LoQ4mfufvgxOx1NVeLFOuaGJBNqHPXZwyiKiA2f\nmE1haGps+NyEeC+N2QqCUPKT/+kNfu30LH/1k0d4++99Oy997tt4/kiZv/UbZ/ittxbu9xDvCRu5\nk34K+CjwjJSyJKUsAR8GPiqE+KnbvLdGJIX9TQAhxIeArJTy44AuhHjmzoceExMTExMTExMTE7MR\n/sWXL/Cld5b5O3/8Yf7X7zhBPplgvJji5//8UzwzOcDf+q9vs9Cw7vcwt52NGD9/DvgBKeUVIcTz\nQogfBD4G/Bbwk7d6o5TSllKub7v8HPB7vZ+/DHzkDsYcExMTExMTExMTE7NB3ppt8K+/conv/dAE\nf+ljh655ztBU/tmffoJQwj/4wrv3aYT3jo0YPwkp5aoQ4peBf0pk+DwDnAA2K99VBFq9n5u9369B\nCPFZIcRpIcTplZW4tGi3YXsBLdu738OI2YG4fthXc4p5MPCC6JpvtKVCzP0lDCUN08UPwvs9lJhd\nQtPysL24KfNOJwgln/uNMwznkvyd73r4hq/ZX0rzo584zBfPLPHOQvMej/DeshHjx+39+zTwUSnl\nj0kpf0JK+RPA1U3+vSaQ7/2cBxoffIGU8uellE9LKZ8eGhra5MfH3GvqXZeuE3UFN91I3vaVqdp1\nyiFdx9+13cO3G9cPWe04BOH2HBB3gkHqByEvX6ny6nStL9kas/vYjDEThpLfP7fMixdXOLcYX/Pd\nwFtzDf7w4ipfOV8BooOt48cH2wcBKSVN08PbhOF7fqnFH5xb5qXLq7EBtMP5b2/Mc26xxd/+Yycp\npG7eiP2HP3aIfFLjX3/l0j0c3b1nI2pvp4QQLSAFtIQQa7ueAJKb/HvfIJLJ/nXg08C/2+T7Y3YQ\nM1WTC8ttFAWePTSI7QX9A3zbft/QqXYc3pyN7NwnDwzEstcf4PR0DdMNKGV1PnRgYEs/23IDvnml\nShBIjo/kODB4fzq4e4HE8aJNtR1HBnclYSh59Uo0V0cLSR7dV7jl6y9W2pydb6GISL74fb9XzE5l\nteNwfrkNAhKqwA9AUwUfOTIYi1fscd5dbLHYsEnrKs8dHryt8IQXhHxzqsZK26Gc1Xlyf0AyEc+R\nnYjtBfyz/3GexycK/LHHxm752kIqwfc/e4BffPEKyy2bkfxmj/m7g9tGfqSUqpQyD7wI+EQGzFeB\nrwC/c6v3CiESQogvA6eALwEJwBZCfB0IpJSv3N3wY+4nphcZOGEY3VyDGZ2Dg2lG8kkOD2X6r2vb\nPlJGXao7dhz9WU8YSuyeZ9Vyt95zZnkBQRAZpPcz+pPSVY6P5BjKGRwfvbNmtzH3l0BKzN4cbW/g\nPvZDycRAimwyweRg5ravj7n/HB7Kkk0m2D+QptaN1gs/kNhenAa311m7p003wN9AFoIXhJSzOsV0\nglLGYCB2au5Yfu3VWRaaNp/7jhMbUlP8oQ8fIAglv/ryzD0Y3f1hw31+gL+32Q+XUnpEEZ71vLzZ\nz4nZmRwqZwhDSCaUfoO6YyPXH2wnBlJ0XR+BYLy4N70Id4qiCB7dV6DScpgYSG3555cyOpPlNKYb\n3Peu4AcG0/ct8hRz9yRUhZPjeVbaDgdLt7+Oa/Mto2scLMfGz27gyFAWTRF0HJ/RfJK5ukU2qd0y\nTSZmb3BiNMd01WQwo6NvoP9aWtc4tb/IwUGPQ/H9vWPxg5DPvzjFkweKfOTI4Ibec3Aww8ePlfkv\nr83x1z59bE+2pNhMk9MXhBAHgWNSyi8LIdJAHON8gDE0lYfHb5/KoqkKj4zfOkVmPUtNm67rc6CU\nfiD6Ggznkgznts8oPDp8fyItrh8yUzPJJzWG92jo/EFjXzHFvuKtjXQpJXN1i1BKTo7m474tu4yD\n66J0gz2n1p2w0nZoWh77S6k4ZW4XUEzrPJHeXPRmYiDNxECUynyp0mY4nySfjA3lncSX3llmtmbx\nt//oyU0ZMd/9xD7+xn9+i9dn6jx1sLSNI7w/bPhkKYT4UeC/AP+m99A+4De3Y1Ax94dziy1euLBy\nnVjBvaRle5ydb3JlpcvF5c59G8eDRtP0ePHiKqena5sqeL0VF5bbTK92eXuu2RfFiNm9VNo2X7uw\nwluzDcJbpMUstWzOL7W5uNxhrr73+0XsZM4vtXnhwgoz1Xu7pltuwNtzDaZXu7wXi13sKWwv4OWp\nKi9dWqXTW9ffnG0wvWryxsx1GlYx9xEpJT//tctMDqb5zMOjm3rvtz8ygqEp/Nabe7Pp6Wbc6j9O\n1Oy0BSClvAgMb8eg7gVNy2O2Zm7ZQe92hKFktePsWEUULwiZr1t4fnjHxs9szeQr71U4O3+9ROJK\n2+FSpX3b/78qBGvOCU2NPcbbTaVts9CwmKub2F5Aw/SodyOBx3OLLb7yXoUrq91NfabjB6y0Hdau\nnqKAchuP02LT4vJK557djzGbZ7Zm4fohyy2bP7y8ylfPV6i07P7zUkoWGhZ1M5o/lufTcTZeZ9Y0\nI+/xgyqIEYaSlfbG9gjT9Zmpmrd8bRDKaI/zQ67Wbn8Ph6Hk9Zk6Xz1fYXnddV1jtmZyZbW7IVVK\nIeiv42oc+dsywlDyxkydr5yvsNS8/hrdC1Y7Dm3bx3QDlpqRc6Nj+1Q7DjfbspumR9Pa3H0d7wl3\nz8tXarw11+RHPn540/dhLpng204M84Uzi9umRHs/2UzNjyOldNfCZkIIDdiV34jjB7x2tUYYQt10\neXziunZDW867iy2Wmja6pvD8kUG0HZbOlVAVhnIGK22H0cLmU5SmKh1+9dUZ0gmVE6N5DpRSVNou\nxXSCrKHx9lwDKaOiyidvoWiWMTSeOjgQKUrFqVJ3TKVlc2mlw1DWuGEdFkQqfG/PRoZqOaujKgLX\nD7hS7dJxfOZ7Xvu5unlNTncYSmbrJooQTAykrgmlSyk5PV3HcgMGMglOjufJGhop/eZpLw3T5Z35\nqP2XF4ScGI1Vwe4nthdwdr6JEPDovkI/ZWmskKRhuiQUBcsNUIRgrmExnE+y3LL4wttLeEHAkaEc\npXSCK1WXhYZNIa1vKFXujdk6fiBZbjl89Gj5XvxXdxTvLLRYbtkYCYXnj5RveVg5PV3H9UMWmhbP\nHb5xHr+qCIbzBpWWw1ghxVLTZr5hMTGQuqGCU8f1qXUiw3W+YV3zmkrb7kvUB2GI60ualsfxkewN\nU+OSCZWnJ0u0bX/L1nEviBxzaV27oz1qL9B1far9a2Te1fdwZbXLXN1EUxUODWY2/FmDGQMjERnB\nQ9kkp6drXFntkjE0yrnr50KlZfP2XLTPPHmguKFUynhP2Bp+/mtTDGZ0vu+piTt6/3c8OsrvnF3i\nzdkGTx3cWiXa+81mjJ8XhBA/DaSEEJ8Bfgz47e0Z1vayvkXFvTJo1zx0rh/ih5L7mQJtOT6//14F\nIeDx/QU6doDjB1huSD6lMV5M4QUhX7uwwuWVDsM5g8GMwUQpfcPCRtvz+Y035pmqdNA1hYdG87y7\n2KJl+aiK4JnJEooQBFKiKbc3+oppnWJcF79p5hsWZ+eb6KpAVRQ6tk/b8jgwmObKapfFhs2BwXS/\nEH393C+kdU7tL/LKlRpty6dt+Qxmdeqme93Bda5u9VMSNVWw0nY4PV3jarXLWCFFMZ3ACySXVzp8\n56PJ2xZLq0oU7ZOSDc2PmM1R7Th0nYB9A6kNef8WmzYN00MieW+xRdcJcIOQIJQkEwpP7h/g3FJ0\nf48Xornxe+8uc3G5zdWaSdv2+bYTw+STCeYbFn4QMlc36dg+Y4Uk78y3aLs+nz4xzIFefYkQAk1R\n8IMA7QGNFFjr9ogglKiKoGl5NEyX0UISQ1OptG3OzjW5UOkwOZgmXLeZXaq0qZseJ8cih4PtBdhe\nSMZQGS8m+eZUFdcPef1qjWMjOfwwivaPFVI8dXCAgbTOQCZxzXVtmh7vLDbxAkkYShRFREZXI4o6\nTFe7Nz3M5pOJLa3/uFTp9B0yKV19IEUYUppKteuw2nH41MkRIHLmLjVtimn9tt+J5QYkVIEiBJcr\nHebqJk3Lo2v7pBIqaUPljZkGpuuTT2p03YCJgWv3/ZSu8vFjQ1FfIMtjpmpiugG6plxTo9uyPaZX\nu8zVTN5bbpNOqDw0kmPwJro7C3WLL55dJGNofOrEULwn3CUXl9v8wXsVfurTx+9YgvyTx4dRFcHv\nn1t+oI2fzwE/DJwh6tXzReDz2zGo7SaZUHli/wAN02XfNihs3YgTY3muVruUMvodTUTHD2jbPqW0\nvqEC4jUv375i6jqPzsvTNd5baqMpglBCKqFydr5B0/I5OpJlNJ9C1xQur3RoWT5XqybPTpbwwvDG\nxo8bstyycPyQ8WKSkZzOl9+r0HV8nj9SxkgoPD05QMv2GbmBZyjm5tS7LjM1k+G8wVjhxnPV9gLO\nL7U5O99kuWUTSsglVdp2wEBaBymZq0WHhrfnmszXLQbSOo/uy/PIvjwd22e1Y1PrOqR0lbbtk9ZV\nTk0UbzjX1HW5DbWuy2zV5N2FFnN1i2rH4+PHy3h+yFghyZVq97YKb7lkgqcODmB5cbRvq+k4Pq9M\n17DcgI6T4+F1wiN+EPLWXIOFhkUumeCh0RxjhRRtu5cSHIZM99KcMoaGpkTR4WrXva4ANp9MkFAF\nWV1jrJAkl4wM4PmGhRuEnF9sM15M8fZ8k6lKl9WOg+X6/JVvOdo3yJ46OEDNdClnt0Yy1/VDzi40\nCULJo+OFW0YfdwInx3JcrZqUswa6pjBXM/mNN+bJGCpPTBR5arLEctMhlDBeSDKcMzgxlufySoeZ\nqskr01VUoVDruvyRR0ZZaTu0eqlGCw2bQirB1EoXCczXLeqmh+0FJFSFxabNYNa45rpabsDZhSam\n4yOE4NBQhnwqwWBG592FFk3bY7J87xqRrxnFQjx4qXSm63NhuYPrB5TSOoMZA8+XVNo2/99bUVrS\n4aEMnzg+hCIEddMln0xco9p2tRrV0SYTKh8+XGIwq7PQsMgnEwgRpSc3TK8/Z96abTJeTPK1CyvY\nXsDxkdw137sQgpSuMpgz+rL2h9aJZVxYatMwPaZrJnkjGks+FR05ozT4DgOZRD+q88ZsnYbp0TA9\nVtpuvCfcJb/w9SmSCYU/95GDd/wZhXSCZyYH+P1zFf7md5zYwtHdfzaj9hYCvwD8ghCiBEzIjbT5\n3qGUMvo9bbaZNbRNKZ6tJwwlr1yp4Xghw3njhml6XhDi+iEZI7qk55ZaBIGkZXnXGT+phEo+FXkG\nB9MJZusWLdPF8kKWmjYDmQQJVWE4l6TrdDhUzpBMKNcpktlegKoIltsW5axBPpng48fK1E0PBYEQ\nglxSw9BUDE0lF6vA4AUhoZT9VKIwlCy2bFIJ9Ybz8dxiC9MNWO04DOeSN9z0r1ZNVtoOfhjSdQNG\ncgbZpMaBUgZNEdi+ZKKUYqERGUCuH3Kp0iaZUDgylMX1TZpmVLh6dDjLZDlDOqHe0PBZ6+f02ESB\nmVoUTVrtuhTTOqYboKgwUUyTS2lUWs6GN65iWmf7k08fPIJQcmG5jedLEqrSN35cP+TdxSZXV01m\naia5lEYoJWk9um7jxRTzDYtyVufySpcDJZ2EppDWVQYzOvMNC0OLJO5tL+DjR8sYmqDjBJQyBkM5\ng+F8kq7rE4SSbFJDEYJHxwtcWm4TSEkyEXmx19aVlK6yT986Z1SlbV+TxnV0+P5Kvd+OXDLBkaEs\na47umZqJH4TUuyF102W2ZjKUi6KxxXSKU/ujw+GVlS4106XWcRnKJfv5+YPZSLI4kJKhrMHhcoYD\npTTvLrRoOx4pQ2W+ZhGEksEPrD2OHzVHrvWihkeGsxwczKBrCpWWTT6VQBWC+YbF/oF05ETbZuPy\nyFCWjKGR1lWyxmb8trufqZUuq701HgkJTVDO6lxajmpi2rbfzy55c7ZBveuS1lWeP1rGD0KWWjbv\nLjRpWj4DvbX6if1FTozmqJseqUS0PycTIbmkhuUFPDSaY7raJQwl83WLrKGxf53EfRBKXD/kuUMl\nOo5P1wkwvaB/bTKGRsP0mBhIkdE1BrNG/wwwXe3SdXy6TqTqmtY1jg7nuFDpkNQU9g2k4j3hLqi0\nbH7zjQX+zDP77/qc++mTI/yDL5xjtmZec/13OxteQYQQXwX+RO89bwIrQogXpJR/fZvGFtMjkNEi\nA9c2wpRSUmk7gOT8UgfXDzk2Em1S+WSCetdF1xTqXfeaBmSHhjK4QchYPsWZ+QaqKkglE6QMGMkn\n+wvUn3xiHD+I6jvatsdEMcVMtUu16+L5ATXTo+NEKVK6ppLSNQYyBmldZWq1y3gxxYHStZEiLwh5\nd6EVyeCO5R+ojtDdnhc+DCWPTxQZyhlMrXaYXo0EJp49XLouTSRjaJhuQEpXuZmzs5BKMAscKGX4\n6NFBlpoOqhC8PdckY2i0bY9CKkFG1/CCkP/+xhxV08XQFGqmy0QxFXn+hGAgoxMEkqrrMpwzrqnn\nqXdd3pitA3Bqoshq22WxaTFWTPEnTo3x3mKbmuli+wFPDBV5ZLzwwHlodxrJhMKBgTSWF1DOGv3U\nqE5PVXG6alJKJ9hfSpFPJrBcH8cPmKmZNEwPhOSPPz7G0eEslhcJWZxZaNIyPWqmx2Q5Tcv0uLzS\noZQ18IKQh8fyFHuSuU/uL7LScTlQSvfv9cGMzttzDdKGtq2yuMW0jqYKQnn94f5Osb2AS5UOKV3d\nUN+sStvGcgP2FVO3rfOstGzOzDdRhOCZQyX2l9KsdhyklASh5PxSm1JW5xPH34+2GJqCG4SEYfS9\n51IaH+vVSzVMj0PlNOOFFGrvbw/lknz0qM58wySUUEwmCCXM1k1atk8oJceGszheyGo7Sq8qZXSe\nOlDsRxEiI0dypdrFCUL+6+tzDOeSTJRS21qboSiC8dvUju1VCqkElyod6qbLJ48PsW8gzemrNebq\nFklNYWwky6PjBebqFk0rMvhtPyAMJS9dXuX1qw2ma10mS2kKqUQ/PS6la6T06Bi40LCodlxOjOVR\nBDh+yJHhLGfmmoCkbrpoqmCskELKyCHbdaL06JW2A4CuKXzi2BBd18fQFB5ZV/O5fv6XMjpXVrqU\nczrJniPwodEchwbTqIroz9eYO+OXXprGD0N+5OOH7vqzPtUzfv7gvQp/4fnJux/cDmEz7pOClLIl\nhPgR4JeklH9XCPH2dg0s5n0SvT45qx3nmhSi6arJ5UqHruMjRNR0rGF6HByMDh3LbZt3Fpq8drXO\n4XIGKSCdUDm/3MYPJN+8sspS00ERMJo3GC2kSKgKUkqEiCI3bcdjaqXLO4tNLleuIAQcKWe5tNLB\n8QI6toftS05NFMgkNVbaDqoiKKY1LlW6nF3QySa1vvdhtmbyjalVwjBaKO80GrbTCEPJOz2P6snR\n/A27XTctjyCIvLIN02UoZ7BeyEbeQNTmsX0FmpZHNqldp9Ff7Th4geRqtctIwUAg+NI7yyQ1lbbt\nEYQSSbRJrR0y35qr8+LFKmEoqTRt6qbPeDHJ9zw5wXOHS9h+yGtXIwPn6HAWI6FwqdKhnDXI6Bph\nb4xXq2avdk5gaAoDGYNC2qHrBkgJjhcwtdJFiKh53o0OfucWowLvI0PZPeVRutc4fiRQEITRfFnv\ngdcUheeODFLtRMbsNy5XQUQHnYblMTGQ5qkDBZ44WKLedTk736LStrm01GapbVPtpMgZCVIJlXcW\nmrw+02C+3iVEMJQ1+Oq5ZRbbNpmERjGtMZxPMZw1+GR2mISqcG6pjekErLYdnu8dyseKKco5g6VG\ntD4dKGUYukU67GzN7NUeJjfUV2yNrKHx8WNDhFJuWb+wyyudvsrWQPra7IG5uokXSA6W0iy2bM7O\nN1hs2kwUI+Pz+HCOqzUTRcCBUvq6+7lpeUgZObvatsf+UpqBdIJKy+HNuQZz9Q4CyWrHZjiX4tRE\ngWrHxfEDLlY6CAQHRJpLlU5kwFZNckmNqZUuKV3j2HAUOfnyuWXeXWhFDacFDGeTLDVtzi22sf0A\nKSVeECnPLTSjyM580+4be5Jo/OeXOv1D73AuSa2nErkePwh5b6lNEEpOjOVu2e+naUbpVoX0zswQ\nWEsRvFOHjulGCmmDGX1D/VaklJFhWjNZaFhYXiQeMbXaZShnYDoBpYxOvpzmqYMlvnZxha7t44Uh\nR4eyjBSSKIpgaqXLW3N1Fps2o1mD8UISLwj790QQSi5W2rw508DQFF66vEJa10BEqYYDaZ2O7bPU\nchjOGahCUEzrvLfYom65BIEkbWikEiqHhjJIKXl9poHnh9h+QFJT6bo+x4azHBvJkVAVHC+qL5YS\n3CAkqUTzYr5pU+u6HBnK9B0oH8Ryg74T7on9xWisMX06js+vfPMq3/no2DU9u+6UQ+UMBwfTfP3i\n6gNr/GhCiDHgTwN/e5vGs+dYbEbysPsH0ptq9md7Aa/P1JESTu0vMlpIXpe+5vdOzhlDI5/SUBWF\nw0PRZFcUga4qIAVhKHnx8ir5ZIKMoeIFEl2NihNH8gYrHYcn9g/01VouVjpcrXZxg5DhnEHTcnnh\n/ArVjoOUITNVE00RVLsuTcslqSlU2jb7ilF4u256vZz+KC3O0BQ+fHiQrKHRdT2mV01CKTnRyJFK\nqIzkk/10vd1Ky/b68rAzNfOGxs9wzmA1b+AFkomB6LB/ZChKJUklVDJGVNBcTOkIERkHUsLJsfx1\nh7cXL61w+kqd+aaJ50tcP8D1A5bbLuOFFMWURtsJMBIKi02HtKFwcjTP27MN2nZ0yGpVHDpOyFzN\n5LlDgzh+VAPy5mydlK5hJKK/udJyWGra/JFHRhjJJ5FI9pfSNKzIo3+s10T12EiWlB79P5q23/8+\n8snEdXU/a9Lqa99XbPzcGU3LY65mUu/2ajuaVv+QOlszOb/UJp9K8PTBARabNjM1k5btkTU0FATD\nOYOT40VsL+C331xgtW3jBCEzdZOG7eGFksPtDJ//+hRvzDXw/QBVUbC8gKmVNoYaXeswKdETAjcI\nmG9ayFBStRzemm3QdXxSusqxkVzfyFGE4PxypB5mue3bGj9+EMloHxvJbsqQURWB4wacmW+S1FRO\njObuqunqWkqPqgiSiffHUWnb/X42oZQsN21cT7LadhnNJxEI5uoWlytrQiFKX0ik3nWpmQ6uH6Iq\nMJSNXm97Pq/1DpEt26OU0al1HFqmj4JDvesxtdLB9gIsN6r7m2uYNG2XYkrnv74+h0BwcjzHtz00\nwtRqp5fmGu0bXSfgmUMD1Loer12t895Si0fG86y2HfSEysRAiqbl4oeSYi9S0DBdfufMIh0nIJlQ\nMHqOlqnVLt9y/HqFvq+er/CNqSpHh7Jkk9pNo2Ur7WiuQCTCs51Nn++E6dUulyod0rrKs4dKm1Zr\njXrj1AhCiZEQJDWVbFLj4GDmhod3Lwh59UoNywuiNMeUjuUGDGWVfhTlai0a07ccHyIMJY4XcGG5\nTVpXeXisQKXlMF+3GM0ncbwo+ukEAW/ONZipm3z40CDD+SQXltu8drXG2/NNwiCqFzU0FU0V6JrC\nucU2UkoUIVAVeHehRcuOWiIkNAU/8FEFrLRtnj1UYr5hYbo+rhepEY4VUlyqdHrpmILjI1lmql26\nrk8hpfdFOyw36N8fF6XkmckbN9astG1MJ8qAqbQcJsu7++yw1fynV2Zo2z6f/cThLfvMjx4t81tv\nLlxjNO92NjNr/j7wJeBFKeWrQojDwMXtGdbeoNpx+nKNfig3lCaxxkrb6d/gyy2b7A3ee6icQekZ\nFxMD6X5qRMdpoQoBIvIKN0wPx/P5ynSd8WKKP/PMfoSAYjvBF95eZDhnsNRyUIXD9EoX0w9omh6V\nttPv/dA0XWpdh2RCJQhCgl72nSYi0YScrqFrKq9N1xhIG8zVuxiahuMGfOW9CmfmmhwdydDo+mSN\n6GB9tWYSEqlL7XZpW9MNuLjcRlEEx0dvLC2tqUq/Xsv1Q87MNVEVwUOjUSHp6ekaDdMjratMDKSp\ntBw6js+F5TYPjeb6RtCV1S5fOruE7YW0LB8/lDh+wELdxPJDTDfg0GCGhCqYqXYoZ1PkkhqPjQuK\nqQTVrsdo3mCuZlL1fGRCoKmCmZrJl99dptZxCKSk2rFYbDq0LJ9H9xV4ZnKAxybej9Q9d3gQ1w/7\nHrqEqvQFMarC6ff6WCtyhegQ4PghhVRinQzvzjro3C9cP+T1mTqOH3JqonBTz+ca1Y7DGzMNbD/A\n612H9eld7y21WGrZ+GGI6QXMN6yonkRKzi+1+p7aoZzBl99d4uJKm1onSpE1Eiqq45NQFebrFm/O\nNgCJlAI/CFFQcP0Ax/VoWB5CRhGdluVxbrHFv33pCo2uy2LTJqWrDKR13ppt8NyRyAmiKlE9YNv2\nKa7z9Lt+iOUG13j/x4opLlc6DOWMO9p4p1fNfu3PUM64paF1Ow4OZiimdIyEck3KrqYorLSj+3W8\nmKSc1Xl3scVgVufYcJbJcpZK+/2+LImeaIjtBbxypco78y30hMKJ0Txtx+PN2QaVtoVAMDmYYSyf\n5PxSm1enawxmEnzno+P82umrXFk1Gc0nGS+kaFsegYwiyLYXRjWXhkq94/LeYosDg2keGc9zqBw5\nGg4PZZAS3p6t89pMnXrXwdBUHhq1KWUMMrrKoXIU/V2LVHztwgpvzTXJGpHTKp/WyKUS5AyNyytd\nVjsuk+UM+4opLNfn1ekayy2HruPzmUdu3mRxfb8i291cXxfT9fvNNZ88sD2RgGovqmW6AZYXkNvk\nPHR6Cn5hKLmw3EVTFFqWx6MTBT5yePC69O+rq11euryKlFBMJyimdIbzBl3bx0govDpdxQ8kg5nI\neWl6AY/ui7JD6qbHl95ZRFMEmqoymjd4aDTLjkqj5wAAIABJREFUlWqXhaZNw/Qjw6sXcX1vsc2Z\nuZ6in5QoioaqCk5NFLlU6dC2PPK97I2jQxnemm1xZr6JH4YcG85Fkvd1C4nkl785zSePD+H4IU3L\nQ1FguWlTyujkDA1NFbw+02BqtUsgJaf2D/Svl65Fhp3lBv20vCCMoqD5ZKLvtBjMGlHmAdxQWvtB\nxgtCfvHFKzx3uMSp/VtXMfXxo2V+9eUZ3p5rXCd2s1vZjODBfwb+87rfp4Dv3Y5B7RXWh7Zv1+Tx\ngwxmow02lFHEAKLDzmLTZryYopTR0VSFycEM83WT33tnCS+QJFTBVLVLRtdYbTvsG0hx+moNz5e0\nbJdsUuXKaofnj5a5uBwdKHwpubjc5p2FFg0zCm0XegevlbZKy4qKljVFQVMEXq9gPwg8simdA0UD\nLaFF8qyqwrtLLUYKSZ47VOLKSoeFlk3L9gDJoV6K03gxidPzQG7yq9mRLLdsjo5kCUJJ4RZ1DPMN\nC9cPqLQcqh2H6ZrJH15a5dMnR7C96Ptw/JBcUkVKybsLTdq2zwsXVnj+yCDPHynzwvkKxZTGW9Um\nB0tpBjI6bhDQcXzCrkc6ERWvLzYdnECy1LRQSEUN6YTA8QIapks5b9B2AobyBpKoqHal5bDcspFC\nstC0aZk+BwbTeH5IKN9XnxvJR5HIm53PB7NG36Bd29htL+CbU9GmfXQ4y+MTxX6KZUzkVe/YkfDE\nQsO+rfFj9+6fpKZycjTPZDnTT8npOj4ty+e9xRaLKZ1vPT5E03R4d75NpWPheAH5lM7VatT8Mq1H\nwiSDWSOaS3ZkVHdsn3NLbSwvIJSS5w6XGUhpfONyDcfy6HoBSNCEpGW5IAVK1+XsXJOxQgovDCll\n9F6thmR6tQMIjg5neXqyhOn6/WiKF4S8fKWK44UcGExzvNef6lA5w+Tg9WliG6WYTrDQsNBUcceF\n8rWuS7UTrac3SstKJhSSCQVNTdC2vUj6V1UYLSTReqlSY4UUmqKw2LBYakYiJ0EoeWexxXInagp8\nbDhLrRsw3zB5Y7bBYEbnaq3Lx46WOTPfYLltM1e3UIRgarWLnlBpWR7f+9Q+Tl+t4YeSclbnkX0F\n6l2HmuXxxESBfEpHVxXOLbR59WqNdEKj1nXxA0kQhnRtn3QiqgnM6hoCGMgaNG0fq3fgX2pG406o\ngpbl8fBYnodH89TMSCp5sWFFvWNqJg+NRemJpYyOIgQHBzOUb9HfZV8xheNHKbObVWBdaTv9Wtjt\nigQcLme4GEoKqcQdCfcUUpGaYtv2SRsqr1ypMVs3sbyAhBCMD6Q4vJZWKCVXa2bP+QgTpTRpXeGl\ny5Ey42gxyTMHSqR1hYYZpVmuKbSdmijw1fOrtC2f95bbTA6mKaUTnBzLR4IEbkDL8jASKpJISrqQ\n1jgynKXR9UgbKoMZg7oZOToVAflUgpW2wyP7Ciy3XF6brjK12qWcM1homuiqylIrqhdKJVQuLXco\nZnTOzjdJKApHh3N85uERVEUwmk/yX16bxXQDhICsoXJ2vtmvofvwoVJkXPa+49eu1mlZHqWszod6\n/QGzhnZN3VvM+/z2WwssNm3+0fc8tqWf+5EjgwgBX7+4+uAZP0KIfwL8A8ACfhc4Bfw1KeWvbNPY\ndj2lTNQ7xQ1Cxjfp3U7rUb76et6eb0bF6F2Xbzk+xGrH4cvnlnlvISo0NzTIGAkGswZZPUqJuVLt\nstKycYKAIJDM1Cy+fnGVEyNZVjtRQesT+4uoCvzeu4vM1qye3GqSEyM5uk6k358yVGw/AAQyjFI7\nbF+C7+Fj8NzBARw/ZGqly3gxSba3eHXdkErLIZPQUBRBPpngo0fKZJPRAX217d6VJ3ansK+YomF6\nDKQ1sskb31bLLZtzCy0ur3QQAtqWz0yty3A+yQsXVvjuJ8eZb1hkDZWvXljFsn0EkveW2iRUwRsz\ndeZqJiOFJEstl/FimroVHXJNL+SR8RwZI4HvB2SMBE3LY6lneC60bL52ocLVmkWt67HSqx87VM6Q\n1lXmGzaD6QR120PTBJ4PruejKiCRHBvJ0DA9fvedJcpZg9WOw1DOuC7/3fEDLi53MDSFo8PZaw6s\nthfg92qeOk50yI8Nn/cppnUyhobjBxuKho3lk1huZJSsN3wgcrY4fkC9E8nG/tPfO4/jhUzXOlTb\nDqqqoGtqpCQ5VWMgo3G4nKbSdllomDhBQNN0ySYUap2oYW05Z/AtD5V5earGYsuiZXlIGTk8qrbH\ncCGFoWvUupEqlSLgwECa73xsjJmaheUFLDZshIj6jDw8nr/mIOkFIU7PAdDuGYFr3M08GS+mGEjr\nnF9u8dLlVQ4Opjk6fOPo7I2wXZ8vvr2AogjqZo5nD12/+SdUhXLOwPFCOk7UlmC1Jx2/XtQhSm11\nep8bUDNdCCXFZIKJUopjIznyqQSXK+2omXCti+WFXFnt0LZD2nbkTT+70KRl+6iKwkQxxVuzTVY7\nDl4Qcnq6znc9sY8///whZmomGT36m6oiWGrZLDdtWpbHcttmMKNzeCjHE/st3l1sU07rTFW7PH+k\nTDmrc2Y2anbbMB2+8PYiV1a6HCpHDqx8Smel4/adHJ//+hRd26PreP3eP586MQICJm9Te6AoYlPX\nZD3lrMFsLYo8bNdeMpDRb3jdN8Naam8YSqZXuyy3bKZWOuwrJrH9kNFCkqSmstJx+lkWqoC35xq8\nfrXKhaUugYwi+6O5JAlN4dT+AqmE2m9A6wYBlbbNbD1SakwlVJ46OMBCw+J1pYGmCI6PZjk6nGMg\nneDMfJNQRgI844WoBuyli6u0LJ9vTK3ih5KkpiCJokJn5pusmi51y6Pj+iy1bHRVZf9AinI2CUhM\nz2dqpovp+nTtKCI1kNH7jofjozlalocXhnzp7BJ1y2P/QIpiKjq7rI+qdZzIqPvgehBzPVJKfv5r\nUzw0kuOTD22tcVhM6zy+r8CLF1f5a58+vqWffb/YjIvk26WUf1MI8T3AHPCngK8AD7Txs9pxuFo1\nGc4ZN6xbuNFiHISSxabFbM0ipas8Op6naXm0bZ99A6mbpnakEiqdIOrBAnB+qU2149KwIvU1PxSc\nHM1gaILp1S5dx8PzApZaNkJKfCnxA4k6muWlqSoXK226js98w+JbHxoibURytFJKTMfh9IzHN66E\nPDKeJ6NrdCwfO4giAJ4XSTZnDBUZCtp2QDGt8dzhAS4sdWjZPpcqkaTtwVKaJw4UKecM8qnICFqL\nBhwY3Bv5usP5JMO3kXUOgpCO7dOxfRKawv5SklBKFpoW5YyB5QXM1y0qbZulpkPL9hhM6xwbyUSi\nEwstFAGHy1lOjucw3Wija1s+LdsnqWVIahq5lIGhKnzq5DDnl9r8/rkKM6tN6l0w3RAvCNBVFV1V\nmCynsVyfr1+o9NWcMoaGMKBhCg5kNA4P5ZitWXzl/CrDOYMgiHo6rJ21u47P+eU2WSMqYF0rCC+k\nEmiqgqZGRm8xrTNZzmC6fr82LeZ9dE3hI0cGN/z66MB441TalK7y8Hie335rgdmayfRql8FMAkF0\n+EomBB3bZ6Zu8sqVVaarJsO5JG3Lx/7/2XuzIMnO9DzvOfuSJ/esrL2qq3rfsO8YcMghiRGHIk3R\nQUs25ZB9Ycl02CFfORyhC/tGvrIvZDoUoh2yLDlMRsjhIMVFDErcMIPBDHY0gEav1V37kvt2Tp79\n+OLPLnQDjW3QDWAw80UgAHSgOhPZec75v+973+eNMhQgTjI2OmOOTTvIkoSuSPzhW7sM/RgvFIeR\nOIGqozFdMAiSFENPkWURmpszFMo5jd9/Y4cjtRx5UyVSUwrW+wehLBPNfRCnnJzOc3zaoedFn+r7\nEcYp620XS1PuuPe2R8Hk/+f9e7KpybSGt7DX/mc6aG90xrRGwvvyUV4UN4g5M1tAkSXGUcLFnQGn\nZwsUDJWLuwNqjk7V0ScZJj5xmtEaSez2fZJMHFp3e2P++O1dVmoOlZyOrkrs9yOR2xKnzJfE4VJX\nJdwgYaXq4IYxC2Wbm22XxjA4zHhpjQQauzMK+KsrTSo5ld98conmSCKIUzpexJn5IlN5gcDe7Y7Z\n6Izx4gRTkVlvjfj9N7pMF0xOzRTYaHtsdcakiGn9M0drbHQ8RgMhczw1k+fYlMMrrqBIRkmKqSnM\nlKz7jqXOGSrfOH532XSWZfS8iJyh3pF5c7/LDWLe2emjKRLn50t3vLYsS6xMOewPAhxDZa/vs9vz\nOTJls950ubDdZ6frcW6uCJLYfowCsTGacmxB6XQDhr6QWluawvF6nnrBZKszpmjpHAx8iqbGbNFk\nrmzR9QT5NWco2JpG14t4aa1NwdTwwpi9ns/1A5G7s9ZyaQx8posmP3+6zvevtcgZKm9s9ijbGqos\nkdMVMTzJMrIspeOFzBYtCrbGbMHirc0+e/0xhiYTxDFvbnbRFZmTM3kemC8JH1+acWGzh2MKKXac\nprRHAVXHIMsyrjVGpJn48/2qY+q/CvXC1SaX94f8z7/x4H0ZKj57rMbvfPcGQz/6WsSWfCbgweTv\nvwz8XpZlnZ9ObUUDMg4Tum7I7ETi8En11laXd3b6eIEIDtvujllrjsgyMRE/N393Atqjy2UagwBd\nlbh2MGCtMcLWZJ5arVIwFa43XSxdYa05ZK8fMAoiyrbOdN5kqysyI1Q5Zb8fUMsbXG+IjAA/SrA1\nhcVyDktTGYcxqzWLV9b79McJV/YH2LrQ65qSTNlWsQ2FURgRxAkFS2GxYqFIEtcaQ15b75KSCblL\n0SRIhEzC1FS6boQb9H/i1tZpmnGz7ZFkwtTshzE3mhHlnIZtOJRzGr/7ww0OBmJqXs/rBFFMkmnM\nFCzSNOPKwQhVluiMAopWhZWakAn8n9/fIEoyuuOQTMr4s4v7zJUtpooGj61UeeVmF1WVSFMhGdAV\nWUgOdIWjdYeXrrfpeJEAHGQZg7GEqSvMlSxxmPJFbpMfJaiyxLn5AkenHNxQZDrcaLrs9sZEccrJ\nid9JBOaFbHbGSJL47pZs/acPsc9ZaZpxo+UCGas152PN+1N5k2NTDh03QEJg8bMso5TTyZsaWZYx\nHMdc3BuQZtAfD4niFEkSHsUwTkmyjN2Ox3w1NzEpp5RNndmixf7Ap2grLFQEzEWS4GAQ0PfEVjLO\nhPfEUAVh8tiUQ8XWOTntsDgBYDRHwSH4wlBlTs8WmC4k3Gy55M3oEAxyt7reGB1mV+VN9VAmeGV/\nyJV9sQ3/zrkZTs8VkSSJparNbm9MPW/wwxttQNCiPgm3b+sKRUsTUq+5DzdN+32fd3f6gJDYybLE\n6dk8lq7wxoaQKr2+GSJJEtN54dFYKucYBRGKLLHWGHFyJj+Rb6XkdJ+droeMhKXKaKqCbSiUbI1R\nmDBfsNA0iSTJmC2aHAx9dEVgoNM0Y2Uqhx+JgOM/fWeXV262SDO4vDfkVx6aZ6WW4+RMHkkSG41j\ndYfXN7tMOTqmpoIMf/T2LqNxTMcNeeZojam8wWvrXcI45dx8kXPzRTY7HtebLjfbHramUHN0zs0X\nUSSYLpqs1nJfeh7Ppb0huz1xCH/maO2eofezLGO97RElIvj7gwPL3d74UMLaGgUfQnQ/vVoliFKG\ngSAsHgx8fvvfX6NeMNjtByDBKIxZqIif0xSZp1YraIpCmsGlPRE+2x6FnF8o0vFCHlkuY2rCE6op\nRU7PFJDFLZ3WKCSKU9ojEWybDSTmyyZ+lODHAoPfGATIMhiKzLm5IkVb4+xskTc3elzdH6IqMo8s\nlfilc3NsdFzKtspr6xP/DhES8NRqhRtNl2iCBe24Ef/mrV1+5mTMA/MlbrRcHlkqM1O06IwCpidh\nvSdn8ry7IwA/x6cdCqbGZltEQDiGejhE/qTP/Se5fueFG8wUTH71wbn78vt/43iNf/rXa7x8o8Mv\nnJm+L6/xRdZnuTP9sSRJlxGyt9+SJGkK8D/hZ772VbQ0xmEyCfGDC1s92m7A8Xr+IwlWoyAhbwjN\ntabKH8q6iJKU7e6YnKF8aNK41hwx8EPe3OwzVzSYK9o8f26GV9c7nFYFyW11yiFOYCqv8zPHp/iz\nd/foeKEgsEVCn100NGbyBm/v9NntCmnUUys1vDCiOUjY7gnJRjrKiOMUzRJ0oEpOZ6liEcYQxoIA\n0/didEVo3l+52aEx8gVRTlWo5U3iJCNDYhyJh4F9n8PwvoqVZMJztdP12Wi7SJJElkkcDAP6Y+HT\nCZOYaw2Pak5jpZajMQj4/vUWeVPlwfkiQZSSM1VyhoosyVzY6hOnCaosoAJxmnJhq0d/HKOrMu2h\nz5vrXVRF4tRMgbYbUM0ZbHc98rqKF6VcOxhxvJ5jtzeeSJVkFsrWoYb8+LSDocgM/Ji8peDoGp1R\nyJubXTRFnpCPJK4eDJERUqYnVitosszO5GCaZRz6u+7Z55lmP5EZQju9Mest4dPRFeVDFL1b1Rz6\njIOEx1Yq9MYh250xrZE72eDI9DyxyZguWoRJTJJKVHIajqnQ95PD6X2cArJEEKa0RiFpBktVm2+f\nm+avrjTojcW1X5ugd/e6Y0xNxVBlfu7EFNcaIzJSHpwv8t7ekEt7A/KWyvyEfukYwlydJNmhyfnq\nwZDGQEjDPs5jcWuaLsvccQgqWCptV/gP9gc+pyc4/RPTeU5M57nZcg8broOB/4k4WEtXSMlIgbWm\ny1zpzs98PKGtvbfXJ04F9v/6wYiqoxOlYrgkAzvdMXldRZVlHFNlsWLxvWtNjtUdZCROzeRxwwRV\nkagXTH5wo02KRNHWqOcNvDBlpmBi6cJHsdP38YOYvheJA2xOvF49bzKTN2m6Po2JFyNJwQ1jzi+U\nODtf5ETd4YWrDX641mbkx5yeLeCFYrgxWxI4Yy9KQJKo5gzmShZ/75kjXNkbsNUd8+LVJrcyzmVJ\nUE1bo5COF2JrCpIUMPRjnj1a+1x0vU9b6STY9YOv5U42lEGUEiUpinxvnj3NYXBIJpMlPrRJrDkG\n290xygQVvdXxuHowpJzTqeV0brRcNtojnEkenx+lDPwQL4oZhymOLvPGZoeLOzJzJZssy7A0Qcvr\neiGvrUdi6OqFXNjusVi2yJtiA3urWX1jo0vbDXn1ZodrB0MOBj5JlopcHUlAiso5nSTNGPgxDy2K\nDVXB1Ahi8Xm9sdlDVyR0VZkEnRv8/Ok6HTfk7Z0+v3R+ln/+vRv4UUpmgqpIPLBQZK0x4iANMFQZ\nUxU+oOYw4JHFMqu1HMfrDq+4AUdrDromM+UY7PcnctAoZbqgoKky0QSO82k/95/UurDV4wc32vyj\n75y+bxvORyfN9YvXWz9ZzU+WZf/9xPfTz7IskSTJA/6D+/fWfjzq7FyB5apIKA6T9DD3YKc3/sjm\n5+xcgZ2uxtNHa0wXTBRZ4pGlMkNfkIKuHrw/0XxyVcHSFPb6PqoiEcYpzUFIkqbs9HzCJOPV9Q62\npjDyY0q2ymPLZU7NFJgvm6iyzAvXGkzlDYJYmJN3+mP2BmP6Qcw4SkgmzVa6krLZHtNyA3Z6HiVb\nx1AVcqZKnMnkLQ1Tl6k4Jq2RkDf4UcqNlsufvLuLjCQOVZOJ5CNLJR5ZLHGt5RInGd8+O03O0A6x\nqSDkARttj0pO/xDK+4ssLxSHuM+KMP20pSkyfS9mu+fhxxlTtsZaW2zoSrbOWI0wNBVDEYbfUZBw\nremy2RFUm4Ef8Z8+dURkL/kR7VHATtej64VEiZAfdtyIMMnImxrlnE5jEPLmZo/9gchyeuZojXEs\ntnw7/TGWJk8w6RrH6w7jOKFs6URpxpFqjsWKfSi3WKmJ5PkM2O36pIicEz8SOPQT9TyqLMh/t5r5\nI1WbNMtQZekQ2vFZqu9FtN2A2aJ1R3bN9caI9ZZ7hwn2x73SNMOPk08kVd3Cj2dZRnPkk2YZyx+A\nAdxsubxwtUFnFGJqMpWccZjL5EYJqQ7jMCZKMsK2S85QcXSFuaJFMadTtOFyGJEzNeIkJW9qjAJB\nEjMUhYcXyzyxWuVm2+Pibp+OG1LOaaxMiVyaUZjwzNEqZ+eF0T7NBIq9Nw4J44RX1zucXyiyVBGY\n32eOVomT7BB3f2sTo8jSx052j07lKFjqBBP//ud2dq4oNqFuyJHahzeNNUdnoy1yzKofY8I//MxV\n+RD6sjtpmm6vxbLFjUkOUW8c0R5FaKpElkHF1nniSJk/eHOPk9MqpZzGLx+dBQlKls47OwO6XsiR\nqRx/84E50jTjwnaPK/tDLF1hrzdm2IqJk4wwyTg6ZVO0dSFh0hQ0WeLUXAHXjwnilK2uy3evtXj5\nZpuarRMkGY6lMfIjVFnm1fUOzx6rcXF3wEtrHbww5mbTpWAJWqeuymy0PaqOjqMrrE45/H9vbnF2\nrkjNMbjSGOL6E6VDyWS+bPHwYomhH2Nq4jsky2I4sdn2qOYGnJi+e87XvaqhHx1mkz2yXL5jmHhy\nJi/uFTn9ngZqJ5nIUJOQ7ppdVM6JMFoJ0ZDt9sZkmdgI7fXGbHU91lseU3mDX3t4XtD9BkIO6QYJ\n6+0R7ZHw1OmqSt4Uw1JV8Tg3VxDX9Fj4asJYyMXao5Bnj01xdMqhP44IkoSdrsdfXtonTjN2emNs\nXWHKgceOlMkyicYoYKFkk7c0nlqtHjaPF3f67PV9wjhlZcrhiZWIcRjz/LlZxlHCd6+1uN4YEScJ\nkixRMjXmyxbPn53lB2st6kWTWl6n40YslC0WKhYdN0KWpQmRNuZG06Vs61QdAVg6VncYBWKDNPRj\nnl6tEibpHdtDQ1WQJDFQ+7jMqJ+0+p3vrpE3Vf7OE4v37TUMVeGJlSovXm/dt9f4IuuzAA9s4L8C\nloC/D8wBJ4E/vj9v7cejJEk6nEzKkky9YNB2QxY+hlhTc4wPkW/KOf0wG0aWb/3e4iJ/4UpThFZm\nGUVbI8kyVqsOCRnTeZORHwsMZhgzHEe8tzfgWN2hZOu8eK1J3tAo2+Lm3xj4VByDLM3QVZmiJYzx\nNUccZBfKFo1RgKHJOIaKH6UoioypSMSqRJwIqYwsi3RoPwrwo4S3N3toikyYZJiazInpPKdmCvzM\nyToPLkVca4xouxFRknGj6bJctZkumFzaG9DzIvb6Y0q2dk8fUJ+2brZc1hojTE3hydXKPV2lj4IY\nS1NQZImao6MpMkmaYZsKyzUHTVHoeRGgoCBkMZokockZEiLL6dZ27fL+EG+SjdBxA8o5HT9OOF6y\n2R+MGYcCeZwp8OBCAUdXBJo1jCnbOlkmSHRpkrDeynCDkFMSzJVsVEXmZtPlyZUq3zpd58J2n6Ef\nkQsUxqHIazhad4hTcdgu53QKlsZUXsAPVFn6UI6HqsiHxK7PWkma8cZWl2QStvjk6vtemMYkP6gz\nCr8WuQNZlvHahGo0V7I+Nsiznjd57IjMdmfM/sCn60Zo6vuZMSDAEuIA5dIZRVQdDU2RsQ0VxxAB\ntFGcEaegSBlxmiHJ0mE6/HzZZq5oESUZfpRyajrPetvFDWJMXcbSZaJEyKsOhj77fZ/WMECVZR5a\nLGFoCrNFkyhOURTIqRqzxTzv7g7Y749RFImr+yNyukrVMTBUhdvVUcfrDuWJZ+jj7geSJN3VgyNJ\nEg9/TFOcNzW+OZHdfhrpdt7UeGChyHrLZeUuXiRVETlmhirobWfm8uz3A9puwGLFRpFlKo4Iip0t\nWjim2P4M/YhREGGoYqL+ys02f/buPnlTZbFii2trGIgw6SBipmhjaiqqLPHday2O1x1+7aE5xlGK\npStc2R+w3fHY7/sYmnSIDD425dAahZOcFonff3NHfAeSlDDOUGUBypGkDEOT2e2NyZsqY1Kabkia\nZhNJUsprG13SNONIzebYtENRFkS7ipNyeW9I0dJYqdlc2O5jqDK7PR9dVe6r3PUWtQ7EPeH25qdg\naofxAveqLu8P2O6Irc6DC6WPbKAVWQwrkwnMoOP2mXLEc1qf3BtnSwaPLld4erXKe3tDvne1we4E\nDd0fR+iqylzJwtLkw6HoQd9nZyLlcwyVxjAkTlKSFJaqHm9td5Eyie2ux/7ApzlpjDKgXrBZnXIY\n+QmjMCZOU+aKFqdmi4eNzygQ3mM3TIiTlDBJeWSpxFOrVRxT463NLustVxABbZ2pnIEsi7PBha0u\nswWDnKHQHiU8slzmP3lymTc3u6iyaLILlsZezwcJFEXi7HwBU1M4Usvx7k6fzY7HVtcTr/cB2WTR\n1nh8pUIUp59qcPGTUOstlz99d5/f+ubR++7Fee5YjX/8by+xN8lv+nGuzyJ7+xfA68Azk3/fQaCv\n72vzk2UZVw6EMf/EdP4rbbSSJOmuN1o/EnjJW3jqT6rpvMm7231qjsFG2+VGy2W762FPTIayDKMU\nVqdsOm7IiRmH5iCg50Vc3B1wZrbA6xtd3EBMheJUbGJuNEdUHYNzc3niBIbjiGN1h422S38c8ddX\nGhyv53j+VJ0gSXGDGFuTqRUssjShexChSBlrrSFJkokHZJjghj5hAjVHpZzTOD9f5NcfWeDR5fKE\nkBQx8uNJxsGQWs7g2sGI6YI5OdxEwhj/JcmYup4wQvtRcpjifbfyQoH/vZtM8Y0Nkc/ywCSfxY8S\n3tnp0ffE5uSplSqFycZruWJzckaYo9ccl4Kp0BpFDHyBkX57u8frm1DOaSIvI0rQVYntjsvAj1mu\niMPHas2h44bs9328KMaPxHTaMRUyZAq2ylLVJkNIDd0gpmBpdL0ERZGYccxD9Ol21+fETIGVeg5V\nkdnvjycbRZVUyghCkRvytx9fQFcU9gY+uck25r3dAaoiEybpYX7J5y1p8hfwIXnbci3HzaZLvfCj\n5b581SpJs0OZ4a3v4gfrlnFbeD90gR6eNIFrjSFXJ56axYrN0SmH19c7XD0Y4oUJHVelaKuULY2B\nH5MzVVJJJkkF+EJTJRQJen4IksZWx2OxYvH0ahVNlXlnq8dWZ4yqCMJckoj3862TdQZejKnKXNob\n4JgaW70xjyyWeflGm92Bj60Kad6lPYUI4b2FAAAgAElEQVS/9dAc6x3RpPtRwn7fxzHVD01wJUm6\n7wTIz+JXTdIMRZKYLpoiP21S7VFAmAgpmvMB0/3UpCkb+RH/+rUtgXefdnDDiP/1z6+Rt1ROzxZY\nrFjcaLpsdTwubPV4d2eAPslt+5kTddqjkN3emI4bsFC0sXSFjhtSskQz9fJ6B0fXhLE8Smi5ollS\nZIXyZJhk6wrfPjeNrSn86bsHtEcBrh9imRrLVZNq3qQ1DDgzlz/M5tIVmW+dqh9mTzWGPoYqk6SC\nRDo/yZUr2yqOoaIq8h2N6PmJTBe4Y2t7P2q6YHIwEH62L0I9cCtQOE25K9nzFslSluDlmx06o5Cu\nF9JxQ05O53nu+BRn54oEUULR0rF0hShJBXwmqnL9YMCRqs1TK1WiJOWp1Sr/98vrXDkYYCgybTdg\nrmiiKTKzRYuXb3aJ0hRTlSmYKt+/1kZXZJIsZTpvcYMRqiJjTSRo1xpD4jRFl2VmihazRfNwePLa\nRoc31rsULI1fe3ierY7HRtsDJC5s9zE1heYw4Pi0Q38cc3pGIOtbIx/H0Hh7q8+Nlsu7OwNsXaZi\nG2iKULZstEWswy0FgiRJJKnwBt+q2yNBPuoS/eDz9ye9/o/v3UCTZf6zZ4/c99e6dY978VqL33js\n/m2Zvoj6LM3P0SzL/rYkSf8xQJZlnvQFEA96XsR2R0gN1lveHSGLPw6VZRmvrXeFATWn8+jyJ8t0\n3tjsst722Ox4PLBQZKEiAuOmCyY7/TE9NyZK0snhWBx2mqOAwTiiltdRFYn+RAZxcWdALa/z7m4P\n108Ik4yipWJrKgVTGJ9Xqg4Xtnt4YcSN5phyLma2aLJYsRlHKRIZWz2fJBUHJC9KsTUx/c1bKm4o\nEqErOY0jVYefOzWFpcu8udXl2FSeSk5ns+OiKjJzhkUYifcOcGa2QL1gUDC1+yqN+Lg6WnNI0yGF\nj/EX9McRr290SFM4O184nHrEScoP1lpc2R+xULbY7fkYqsJfXWnw7k6f+ZKFokiosgiLPFbP8e6O\nmICfmSvyX3/rGFmW8deXm/zwZhvXjxlFKWEs5GTnForEccaN1ohL+wPiFPp+zMPLZWZLFnGa8dJa\nm+bAJ0MkcstI+GFCEGWcmilg6yrbHY/+OCbJxlRyGl6gY+gK1ZzOZmdMexQwUzSQJYm9vi/+21RM\n/sdxTNXWkSXoj2O6nsf2BGTw9NEqjqnSGYXkdPWeUWZkWeKxIxW6bki9cOdBeL5k3bHp+HEvVZE5\nPu3QGAYsf4SH58rBkO3O+JAIN1sUVMg4SXl3EqQsmhb78PCsShJ9T+TTOCMFRVFI0nRC31NYrhZo\nDQVVMJMkgjBmgIStKySZkJ/NlyxeXe/gmCqmJlPO6fTGEdvdMfWCweMrZW60RpRzBu1RyNGpHB0v\npDkMuXEwIpkYlB9eKrPV9Xn+zDTbvTFvbvTY6/uMo4THPiLJ/atSWZaRIfKUbjnXum54GKwZRClH\nanf3De30xnS96JCCeK0x5J3tAWV7gvU1VEDIRTteRJgIiqYbxBRtjZmixUHfZxRmvLbVxTYVZFlm\nvmwy9CMGfoSET8FUhawwyShZGtNFA0WWaYxCgighiMUAzJqES0cTHP5W18dQNZarNsu1HOMw4W+c\ns1FkiV88Pc1bWz0sQ0GRJG40Xfb7PlGacrzu8O2zMx95zy7ZOk+sVoiTOw+396NMTfncGOrPUsen\nHW62XGqTreXt1RoFvLXZQ5JgoWSRJCKkc6szpu0GeGHC8RmHM7PiHOOHMa+vd3h3d0C9IFDp9YJF\nGAswjiLLfPdqi6v7rpA4Rxm9cch+X+c3n1ym4hjUCybv7fbJUtgb+Jyo57m426dgaVRyOt84NsWb\nWz1yhsjxao9CwjhjpmKgKjKbnTErUw55Q+XlG23e3upjajLLtRxn5wrs9X2iJGUwft9ntFpzcEyV\npyYbeTeIefF6i5dvdtjseIyCiDRTGAQhmiJxaW/AO9s9wiTjra0eT65WONjwiZOMd7b7h/eAkzN5\n8qaQ+d2PsNqvWzWHAf/v69v8h4/OfySJ8l7WqZk8Ncfgxes/Wc1PKEmSSKoDJEk6CgT35V3dVroq\ncbMlJBc/im/gy65sgjEFITf5NKUp8qEmf6ZoYukqjyyW8eMEL4i53nQZ+RG6KqOpwrdRyenMFEy+\nqatoisRbWz1xsBlH1PMG7+0OcIOAgq2zWnPojSOWazmePVbljY0uB8OAkR+RkLLZ9nhnp0/V1mCS\nIzFbNBn5LjIS4zDGDyBnaqRJhqUK03KcZhydyvHIUmWSCA/XGfLocoXnjk/0z5J0h7dBlu8uXbnX\n1Rj6XNobkjdVHloo3WGMLdraJx7A3t7q8dZmj+mCyZHa+wfU/YGPFyYEcUpvHPJksUrXFYnqQZzS\ndkPypsK/u7jHVtsjBWo5HU1VGEcJL1xpiO9IKiZ8YZySa7toisTPnqwzkzf5vVe3UCVJmEozUCR4\na6vPAwtFBuOYOElpDMUUfaZgcnRKBIi23ZArE6nc/sBHliRmiybPHK1xarbAQ4sl3CDme9daHKs7\nHJ1yODGdxw1iphyDHVujaGoksY4XJxRMlbKt0xqJyz7LxFT8wYUSg3FE/iPyjX7Ucgz1S6dF3e8a\nhwlvbgq/wkMfk07vTqbJ4cSIrCnyoXS2MRR5XbeHQz65WuHYdJ6+H9FzI8ZxRk5KSDOJwThEwphI\nZTLcMCZwMwqWyvn5wiRgWRCWfu7kFNebLgP3gDDNyBvCU7jd9fiDN3eZKRjU8waGKjNbNHlwsUwQ\nJxyfznF5f4hjKORNIV9bncohSRLzRYvr5ogkFZK7r3qpiszDS+J6mpsMPZLs/fd9+z9/sATsI6E9\nDJnKa/TdiHEUI40z9npjjGoOVZHYaHs8ulRipWrjRwnDIKaU03l8ucxaU4TMmrpovs7PFXhwoci/\nubBHYyAAE2fm8vgNMTAZRzG2IaATM5pJa+TTH4cMvJDmKESSMqJU3BOLtoEbxCiKRE5XOVLN0RwK\nuZ4sS4I8KokGcDpvUi8YSBk8vFTh8v4QSYKTH/D03NqEj6PknkvO7mVlk9yajhtyciZ/h4xnFAg8\nsyyJrcXt26u7ydZvlRvE+HHCdtcjSTPmihaWIXy7b++kGJqEFybcaI7Y6Xq8sdmj50VYmowsI5DV\neYOipU3u3Qn1goGpyWiyTCyllC2dgqWLoNIs48xMgf3+mIIlkNFVR0eVZdaaLm6Q8HefXMINY8I4\no2JrFEwNXZWp53XCJMPWFVqjgAtbXa7uD+mPIwzdQJ3k8j13vMbBwOe9vQGNoSBHGpqMrkhcb4w4\nVnfIGSqPH6kQxyl/6O/SGvrkDZW5gsmL19qEScpsSTTyJ+oOTxyp0hmFWLqQit4qRZY+0iv90/pw\n/cuX1omSlP/iudUv5PUkSeIbx4TvJ02zLwRmcr/qs5ws/kdEuOmiJEn/D/As8J/fjzd1ewVxxmLF\nIk15XwfzY1SyLKRwjUFwiK38pHp0uczlvQGaItEfx6xOOXhhwlJBGJtX6w5pKtbCt5uCb0+Ef2ix\nxLs7fQZ+xDvb4qD8S+dnKVkKq1MFyrZGljEhqmSEccZaY4gXJeLBFSbsRAl1xyCIEh5YKBNEKdcO\nBCQhb2kULE0QobIEGZW8qRFnGSVbw9KVieZcvKfb5UlfxkRnuytQzJ1RyDCI7yDIfFKJDUgi8gfI\nWKq8P+XNTx4kp2fzPLhYwlBlRn7MVMGgGKecnSnwvbUWVw4G3Gh65A0xjdMVkbOy3vYo5zSWykLj\n/5tPLePoCoam4IUx/+yFNdwwQpLgkaUSjaEwsdu6zOX9EZoqms6ZokWSZTx7vMbzp2dwwwR/p89U\nXmezHYsHsKpydq5AvWAiyzBftnEmGQpxCmVbQ5JEBlPOVHh8ucKRao6Ntosiy+iqzE7Xoz8haR2f\ndg43ZeX7PN39ulZjKJpnEJjolY9Ip79FKVMksSW4vVZqOc7MFu44gFq6yj/65dP84z+5xIvXmkBG\n3hI+AklSyLKUjFsSHQlbE6CNNJPI6RpTeZOFss1UweK/+/ZJDvpjRkFM14uwdIVrjRE5Q+W9vYDz\nCyVyhspCyRIBnhJUcga/9vAcrVHA333qyKFhu+OGVHI6Dy+VaI1C5kr3dvBxS7p6+73wXlTJ1u/4\nPWuOwem5AlGcfuxhbRjEnJ8vcXG3TyVnoGsyKzUHL4hpDgPcIGG2ZLJYFlj5oR9PPhcFP0yYyhs8\ne7TGettjKmdwfNqhZGn8cL3DcBRwYatHvWByctrhbz4wy27PZ7fv0fciLE3l9FyeG02X711t4kYJ\nJVvkuszZAldeL5hoisSvPzLPTMFCliWO3+bT01X5ECqy1XF5fbNLTlfpeeFhnlfB1O74DLpeeBhK\nudcf3/fNz49a4yg5pApud+/0MDQG/qFsrzkMPpKq+MGaL1m8uzMQRLUM6kWDc/ki6UqGJEmM/IiL\n2wOmiwYv3xTACTdIODWbZ6Fkc2LaoeNGeGHCk6tC9gaigf7e1RZhIvDzzx6rsDKV493tPj0vYq5o\n0xgG5A2VKwdD2q5PmIjMrTjLqBeEB29lyuFnT05xcXdAY+Bz0A+YK5tEScqVvSFhkrJcszk/V+TM\nrPAe3to6IwnPqKbIXN4bslSZXO9kLFdzVHI65xdKdMcRyzWbMEqxDfF80BWZ//Kbq6iKjD35/qxO\n5ei44Y+9d+TLKjeI+Vc/WOfbZ2ZYnfriIiSePVbjD97a5fL+8GP9qV/1+iy0t38nSdLrwFOINuQf\nZll237EPBVOYYkdB/GN7kXzcpOhuZWkKp+cKBJEwu//V5QZRnHFuocixunNXyklzGNAcigarYGoc\nDAJ+eKNDzwuxDUXotZOUR5frh2GcL99o8+Zml7Wmy4WtLnEKCyWTiq0RJCnWJMRsuzvGMRUUGUxd\nYDKnHIPT83neWO8zV7BxI4Fa/dbJujAAr1QI4vQOCtOXWXNFi54Xkje1z7xNUGSJ+bKNokis1Jw7\nUJJFS+OZo0IHGyYpf/LOLvt9n7mSxamZvMCZK5IwmCeCEOQYCsvVHL/7ww3xZ1a1Dx9auirz4EKJ\nxYpN1xNoYTeMUSSJgqVxZq6EpsgsVix6npjyVXI6miwxX7F57lidlcmNUJYk3tmRiCcLR1tXma/Y\nnJ0vkNOFdvuN9Q6GJqhOtyRroyAmTUGVZeI0Y75s0xoFuEHMH769y/WDEcs1+67m75/WZ6uaI/xv\n4yih5nz0ITFvCrjExZ0B+wP/MDdJyEn6DMYRTx+tcnI6zyvrHa41hjy4UOKBhSKv3OyQZAI9f7xu\nsNZ0BUVKlimaGrIsM13QcQyNqiMgGuMo5q3NLsenncP7V2MYcmQihdnt+ay3XUZ+zMXdPufnS5xf\nKHKtMeJ6Y0h7HNMahixVRUBvydbY7/uHUskPNhP3ovwo4Qc32iRJxupU7r4fCD5Kenlxt897uwPO\nLxSZK1kM/egQ/GGoCrs9nywTQJNbk+93dvosV3KTYYKEqso4loYbiFDkZ4/WWKxY7PdFaOkwiOiP\nI+IURn7CXm9MfxwJuZsCfa/PMIwmEqeUswsFdrs+x+o5wlhkPh2tO9TzBgMv5g/e3OXJ1SoPL5bu\nOs1NUvEzK9Uc8oTqeMsf9MGNb9nWhR80Sr7Sz2xTVag4+mFG3+1VL5js9MbIkkQt/+m/p6oi8+hy\nmXd3+nfEWMiyxNF6jgtbfXZ6HsMgwtEV0iRjdS7Hk0er5HSV5lCoBvb6Y07M5ElTuNYYcP1giK7J\nQsrmGKxM5RmNY9HUJCmzJZPHVsq8dL3F9YZL2TZI0pCVWo7pgpDIvrTWZqPl8ru9MW4gwnGXazZF\nU2e769H3Y5bKOcqOxncemDt8dt8azjQGAclk6DpftlBk+TCfsD0KeXK1ylLV5htZjRevZ1zaHeDH\nCUGccLTuUJgMQv0o4Y3NLludMdEE1FDOfbTk/Kd19/q9VzYZ+DH/4JtfzNbnVj13XMBiXrze/Mlo\nfiRJ+ossy34e+JO7/Np9K1WRefwrrgn/UcoLYzRFvqthW5YlHj9SoT+OcMOYl9ZaZBkU7LsnHYuH\nZ480FTlAjqGy0xtTzWn0xyFTjsBp502NF9cEJeh4Pc9G22O97XFlv88oiMXWIoj59vkZLu0NIYM3\nNnuM44hX1rvMFiyWKjYzRZPTM0Wmiga1nMkLVxucmyoyX7ZQFZnrjRFLFfsr0/gAzBTNz2WGPTNX\n+MgL/RaR6trBkL94r0F3Ejh3dq5I34s5NVOgORAJ22GS8dBimX//3j5v7/aJkow4y0jTjIEfiY1P\nEHOzZVJxDM7M5ul5IY6uocpCGlHPGyxXczy6bNAcBvzDXzhOloqMBVtXeW93QNHWOD2bJ0lTwjjB\nNhSqOQNLVcgbKqMg4er+kHd2+kwXDeI0xdLL2JpCxdaZLZmMw4QjtwUVvrre4Z1JmGOaiofYT+vz\nlapIqIqEhcJOb8ypmY8+APS8iJ3eGEsTkskSwou22xuz2fHoeAEb7RIvXG0wChIaw4BjUw4FU2EY\nJMwUTI7ULDJAk2UkGWbLFvlAIPb9KKVgamiqwNE2hz6NQcDz52aYK1kcqdo8sVqjnDN4arXKfNni\nwlYPL0wYBhF7fV9AWRSFxYp9GDwqHDPvl3SfVvhBlJJMqF9u8MV+N7tuSNsNqOcN/upyAz9KORj4\n/NbPHjtski5s9djqeFzZH2BoCpauUM/rDP2ENM1QFYHefnI1x1LF4qW1NpsdETnwwEKRo3WH//27\na2x1PEqTbXHJ0mi7IaMw5k/f3Z+EtspEacZm22UcJMyXbXRF5pljNcq2DlLGO9vCJzb0Y97e6aHI\nIgT22JRD0dYOr+1b97Y3N7uCHDYMOD1b4MR0ntWpHPJkU3x7aRP63Ve95EnEhB8lbHfHNIfBIWjD\nMdTDQ95nrZmiScnW7lBl9LyQ/jhiMI44N1/ECxOxxQWmCwatYUiLEFNX2B/6jIKES7sD4jTjyv6A\nzY6HocikZIyCmCM1m6lZA1NXuLw34PLlJqYiM44Fxa9g6Tx3vMZC2WJ64pk8N1fk1fU2g3GMrSuc\nXyjyyFKZV252yDI4PZtnKm8yWzTv2NadmslzZX/Ipf0BU45B1dH5xTMzwis4wX2Pb3sW3KK2FSyN\nMEo5PsnXur0OlaJffdXrV7KiJOWfv3iTJ1YqH0u2vB81UzQ5Vnf43rUWf/9njn6hr30v6xNPp5Ik\nmYAN1CRJKvO++KwAzN/H9/a1rfWWy/XGCEOTeXKlSmsUcDDwWarYh/hGUxM6+f2+L4hfoTi83F6j\nIOZ6Y0TeUNAUmSBN6bgB/XHIjaaLY6g8sFDimyem+MvLDd7Y7HK9MeKZYyJXY6Fs8WcX96nmTcqW\nxv5QBJvKsoAZyMA4jnH9GFmSKEyrPLRUpmRr2LpCEKWcnS9iGcLwLsmCCJPTFfwoEXrxr0mNQ6Hj\nruT0j0Rsaop4CHhRwsiPOVLNkWbC8PqLZ+v89eUmcZry6nqH/UGAJEkoUoYxCWMMo5T5sspu38eP\nUqIk41cfmmej7bLd9Wm7YxRFZqZgEkTCeHp6Nn8HZOCl6y0u7g7IsoznTtS4ejDkYBDgGCLDw4sS\n/vxSg4KpkqQpGcJ7QAbjMCVvqjyxUuH0TIHmSOQD3arz80VyusL1xoiZosXKR5i8f1qfvuIkO8T0\njicT1qsHQ5rDgNWp3B2Tcz9KSDMhwVRlieuNIdWcaFTbXoCmyLy52WG755GmsFCxeHS5zPNnZ7iy\nN0JVJbY6Y5IsZbstcp4KlsZi2aI6wdUWLQGtuLw/ZBwmKIrMpd2BOLj5EWfmiry11aNkaTx7rEYG\nXG8MCaKU711rIEsy82WLkqXz2JEyXS9iKm9MSFQajqneN/pX0dY4Wndwg/gO5PrnrSQVuPWCdacJ\n+xYJzQ1i3CBGliVeXe8cHuxKE0jIrWDacRSx1hjh+gklS8hl40RIroIowY9SfuXB2uEEfLXmsN52\nMVSFvKmhKhLjKMWPE/qexHfOzrDd9/HDhBtNl1ATryXkb2MyRFBpPW9wdKrKVtcTgauSIJFFSUol\npzFbstjuenhBjKZIdNyQ715tECUp5+dF8OXewMecBN+O/JjtrveFSm3uZ13aG9AehUiSkPR8nriF\nrY4nwoPzBoNxRNUxUGSJjZbHlX1BWVMVEQZ9S3JnaQrDRMgEV2s5TFVmuzsmTsSzYrfnIyF8odWc\nQZJlSCmcnimQtzT+9WtbbLbFn+2jy2VmiiYr1RwlW6fvi+3fbNHkB2stNtoezaHPYtnGUGXWGiPe\n2uqRphnTBZOlSu5DMkX7VjCvobLdHR/e9xuD4JAmuPKBPK35sslLa20kScgl0yzjRD2PLItm+eGl\nsoCEZOK6/enW57PVH13YZa/v8z/9rfNfyut/41iN33tlEz9KvpR4kntRn2Y0/w+A/xaR6/M67zc/\nA+B/u0/v62tdvQnWNogETvrS3kAEEAbxIUJ0pZZDkSWmCwaPr1RI0ozVDxw2rzdGtIYBrSE8uFgk\nm0gRXt/oMhjHnJ8rkDc1DE3gcbMsQ5YkGv2AzY6HKkt85/zMJJg14y8uNxkFCa+vdwjijChJUWWZ\nkm2gyhnzZYtffmCGmy2PwTimOfR5eGmGZ4/V2OuPORj4/OWlBgfDgKEvMNo/rhfGB+virtBWb3U9\nnjs+ddeNXdUxWa7YWJrwAFm6wkLZ5rX1DmmaYekK1xtjvFBIOM/N5pkqmFiagh+ldNyQ507UMFSZ\nnhdTsm95qiQMTUaSNGxNmFMdQyMdBBNTrUnHE8CB680Rb2/3sHWF+bJF0dKpOjHVnM7DS2Xe2elz\ncaePpSs8MF/i1GQid7PtMlM0GfqC8nZ5f8h+30eW4OGlMkVLYHPPzBU5M/f1aWq/7MoZKqfnCvS9\niJVajjAWwBGAm033jubH0hUWyzZeFHNlf4gfpUgS/OqDc5RtnasHA7a6HiVLo+tGlC2dm22X1SmR\nJ9X1Qt7e7jEYx5QtFcuYGJ8L1kTKKXFqpsCF7R4FS0heC6bYNl/eH2LrCq9tdDlStWlNpuRPH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cVzGcKtKXjNAa8fNTN/TxzHCKVL7CFT0JOqIBvntsjIBtkcpXeP7e9REacV1fksu64+TLDp2x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\nuM92bONv8ZcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2770c4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO：使用自然对数缩放数据\n",
    "log_data = np.log(data)\n",
    "# 等效于下面的代码\n",
    "# from scipy.stats import boxcox\n",
    "# log_data = pd.DataFrame(boxcox(data, 0))\n",
    "\n",
    "# TODO：使用自然对数缩放样本数据\n",
    "log_samples = np.log(samples)\n",
    "\n",
    "# 为每一对新产生的特征制作一个散射矩阵\n",
    "pd.plotting.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算变换前后的Skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before Log Transform: Skewness = [2.55, 4.04, 3.58, 5.89, 3.62, 11.11]\n",
      "After Log Transform: Skewness = [-1.62, -0.23, -0.74, -0.36, -0.27, -1.17]\n"
     ]
    }
   ],
   "source": [
    "# 检查Skewness：\n",
    "# 越接近0表示越接近正态分布\n",
    "# 大于0表示Right-Skewed（右长尾）\n",
    "# 小于0表示Left-Skewed（左长尾）\n",
    "from scipy.stats import skew\n",
    "skew_before = [round(x, 2) for x in skew(data)]\n",
    "skew_after = [round(x, 2) for x in skew(log_data)]\n",
    "print \"Before Log Transform: Skewness =\", skew_before\n",
    "print \"After Log Transform: Skewness =\", skew_after"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将倾斜度的前后变化与对数变换前后的图表结合，可以看到变换前数据分布全都呈现右长尾，倾斜度都是较大的正值，而变换之后的倾斜度都变为了十分接近0的负值，相应的图中的分布曲线也呈现左侧稍长的特点，两者相互印证。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 观察\n",
    "在使用了一个自然对数的缩放之后，数据的各个特征会显得更加的正态分布。对于任意的你以前发现有相关关系的特征对，观察他们的相关关系是否还是存在的（并且尝试观察，他们的相关关系相比原来是变强了还是变弱了）。\n",
    "\n",
    "运行下面的代码以观察样本数据在进行了自然对数转换之后如何改变了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到变换后的数据分布被放大至整个坐标空间，绝大多数都呈圆型，只有少数几个图呈现明显的线性分布关系（$Detergents\\_Paper$ vs $Grocery$等）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 关于对数变换与相关性之间的关系\n",
    "\n",
    "- 首先对数变换本质上是一种非线性变换，而这里的相关性通常指的是Pearson线性相关性，因此两者之间其实没有必然的联系。\n",
    "\n",
    "- 通过计算对数变换前后的相关系数矩阵，可以看到，有的特征之间的相关性减少了，有的则增加了。这也侧面印证了这两者之间的非必然关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fresh</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100510</td>\n",
       "      <td>-0.011854</td>\n",
       "      <td>0.345881</td>\n",
       "      <td>-0.101953</td>\n",
       "      <td>0.244690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milk</th>\n",
       "      <td>0.100510</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.728335</td>\n",
       "      <td>0.123994</td>\n",
       "      <td>0.661816</td>\n",
       "      <td>0.406368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grocery</th>\n",
       "      <td>-0.011854</td>\n",
       "      <td>0.728335</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.040193</td>\n",
       "      <td>0.924641</td>\n",
       "      <td>0.205497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frozen</th>\n",
       "      <td>0.345881</td>\n",
       "      <td>0.123994</td>\n",
       "      <td>-0.040193</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.131525</td>\n",
       "      <td>0.390947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <td>-0.101953</td>\n",
       "      <td>0.661816</td>\n",
       "      <td>0.924641</td>\n",
       "      <td>-0.131525</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.069291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delicatessen</th>\n",
       "      <td>0.244690</td>\n",
       "      <td>0.406368</td>\n",
       "      <td>0.205497</td>\n",
       "      <td>0.390947</td>\n",
       "      <td>0.069291</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Fresh      Milk   Grocery    Frozen  Detergents_Paper  \\\n",
       "Fresh             1.000000  0.100510 -0.011854  0.345881         -0.101953   \n",
       "Milk              0.100510  1.000000  0.728335  0.123994          0.661816   \n",
       "Grocery          -0.011854  0.728335  1.000000 -0.040193          0.924641   \n",
       "Frozen            0.345881  0.123994 -0.040193  1.000000         -0.131525   \n",
       "Detergents_Paper -0.101953  0.661816  0.924641 -0.131525          1.000000   \n",
       "Delicatessen      0.244690  0.406368  0.205497  0.390947          0.069291   \n",
       "\n",
       "                  Delicatessen  \n",
       "Fresh                 0.244690  \n",
       "Milk                  0.406368  \n",
       "Grocery               0.205497  \n",
       "Frozen                0.390947  \n",
       "Detergents_Paper      0.069291  \n",
       "Delicatessen          1.000000  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fresh</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.019834</td>\n",
       "      <td>-0.132713</td>\n",
       "      <td>0.383996</td>\n",
       "      <td>-0.155871</td>\n",
       "      <td>0.255186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milk</th>\n",
       "      <td>-0.019834</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.758851</td>\n",
       "      <td>-0.055316</td>\n",
       "      <td>0.677942</td>\n",
       "      <td>0.337833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grocery</th>\n",
       "      <td>-0.132713</td>\n",
       "      <td>0.758851</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.164524</td>\n",
       "      <td>0.796398</td>\n",
       "      <td>0.235728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frozen</th>\n",
       "      <td>0.383996</td>\n",
       "      <td>-0.055316</td>\n",
       "      <td>-0.164524</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.211576</td>\n",
       "      <td>0.254718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <td>-0.155871</td>\n",
       "      <td>0.677942</td>\n",
       "      <td>0.796398</td>\n",
       "      <td>-0.211576</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delicatessen</th>\n",
       "      <td>0.255186</td>\n",
       "      <td>0.337833</td>\n",
       "      <td>0.235728</td>\n",
       "      <td>0.254718</td>\n",
       "      <td>0.166735</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Fresh      Milk   Grocery    Frozen  Detergents_Paper  \\\n",
       "Fresh             1.000000 -0.019834 -0.132713  0.383996         -0.155871   \n",
       "Milk             -0.019834  1.000000  0.758851 -0.055316          0.677942   \n",
       "Grocery          -0.132713  0.758851  1.000000 -0.164524          0.796398   \n",
       "Frozen            0.383996 -0.055316 -0.164524  1.000000         -0.211576   \n",
       "Detergents_Paper -0.155871  0.677942  0.796398 -0.211576          1.000000   \n",
       "Delicatessen      0.255186  0.337833  0.235728  0.254718          0.166735   \n",
       "\n",
       "                  Delicatessen  \n",
       "Fresh                 0.255186  \n",
       "Milk                  0.337833  \n",
       "Grocery               0.235728  \n",
       "Frozen                0.254718  \n",
       "Detergents_Paper      0.166735  \n",
       "Delicatessen          1.000000  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.702480</td>\n",
       "      <td>10.901524</td>\n",
       "      <td>10.925417</td>\n",
       "      <td>8.959569</td>\n",
       "      <td>10.092909</td>\n",
       "      <td>8.774158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.135494</td>\n",
       "      <td>7.869402</td>\n",
       "      <td>9.001839</td>\n",
       "      <td>4.976734</td>\n",
       "      <td>8.262043</td>\n",
       "      <td>5.379897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.098612</td>\n",
       "      <td>5.808142</td>\n",
       "      <td>8.856661</td>\n",
       "      <td>9.655090</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>6.309918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fresh       Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "0  10.702480  10.901524  10.925417  8.959569         10.092909      8.774158\n",
       "1   3.135494   7.869402   9.001839  4.976734          8.262043      5.379897\n",
       "2   1.098612   5.808142   8.856661  9.655090          2.708050      6.309918"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示经过对数变换后的样本数据\n",
    "display(log_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用箱图显示每个特征经过变换前后的Q1/Q2/Q3/Q4以及IQR和Outlier的距离关系\n",
    "\n",
    "可以看到处理后的特征的Q1-Q3的Box位置都相对处于比较中间的位置，且单位尺度也从10^5降到了10^1。可以把箱图看作是俯视概率密度曲线的投影，箱部就是主峰，散落的outlier就是长尾。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DRlFREbfddhu33norBQUFLFy4EPCWKnvjjTcAKCgoYMGCBTjnmDx5Mg0NDdTW1rJ7927S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\nUMDChd7JegsXLuS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LU1MTe/fuJSsri+zsbH93bcCKifFOcIiKiuKBBx6gpKSE4cOHU1tb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\nCAaNCgAAAAAAEAwaFQAAAAAAIBj/D7/uADSNInU6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2eb4af70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(18, 5))\n",
    "for i in range (1, 13, 2):\n",
    "    plt.subplot(1, 12, i)\n",
    "    bp = plt.boxplot(data.iloc[:, (i - 1)/2])\n",
    "    plt.setp(bp['boxes'], color='blue')\n",
    "    plt.subplot(1, 12, i + 1)\n",
    "    bp2 = plt.boxplot(log_data.iloc[:, (i - 1)/2])\n",
    "    plt.setp(bp2['boxes'], color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习: 异常值检测\n",
    "对于任何的分析，在数据预处理的过程中检测数据中的异常值都是非常重要的一步。异常值的出现会使得把这些值考虑进去后结果出现倾斜。这里有很多关于怎样定义什么是数据集中的异常值的经验法则。这里我们将使用[Tukey的定义异常值的方法](http://datapigtechnologies.com/blog/index.php/highlighting-outliers-in-your-data-with-the-tukey-method/)：一个*异常阶（outlier step）*被定义成1.5倍的四分位距（interquartile range，IQR）。一个数据点如果某个特征包含在该特征的IQR之外的特征，那么该数据点被认定为异常点。\n",
    "\n",
    "在下面的代码单元中，你需要完成下面的功能：\n",
    " - 将指定特征的25th分位点的值分配给`Q1`。使用`np.percentile`来完成这个功能。\n",
    " - 将指定特征的75th分位点的值分配给`Q3`。同样的，使用`np.percentile`来完成这个功能。\n",
    " - 将指定特征的异常阶的计算结果赋值给`step`.\n",
    " - 选择性地通过将索引添加到`outliers`列表中，以移除异常值。\n",
    "\n",
    "**注意：** 如果你选择移除异常值，请保证你选择的样本点不在这些移除的点当中！\n",
    "一旦你完成了这些功能，数据集将存储在`good_data`中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### np.percentile 与 pd.DataFrame.quantile 的用法不同！\n",
    "一个用百分数，一个用小数。\n",
    "- np.percentile（data, q = 25)\n",
    "- df.quantile(q=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Fresh':\n",
      "[65, 66, 81, 95, 96, 128, 171, 193, 218, 304, 305, 338, 353, 355, 357, 412]\n",
      "Data points considered outliers for the feature 'Milk':\n",
      "[86, 98, 154, 356]\n",
      "Data points considered outliers for the feature 'Grocery':\n",
      "[75, 154]\n",
      "Data points considered outliers for the feature 'Frozen':\n",
      "[38, 57, 65, 145, 175, 264, 325, 420, 429, 439]\n",
      "Data points considered outliers for the feature 'Detergents_Paper':\n",
      "[75, 161]\n",
      "Data points considered outliers for the feature 'Delicatessen':\n",
      "[66, 109, 128, 137, 142, 154, 183, 184, 187, 203, 233, 285, 289, 343]\n",
      "\n",
      "Samples categorized as Outlier for more than once:\n",
      "128 -> 2 times\n",
      "154 -> 3 times\n",
      "65 -> 2 times\n",
      "66 -> 2 times\n",
      "75 -> 2 times\n",
      "\n",
      "Sample Size shrink from 440 to 435.\n",
      "\n",
      "Here are the outliers:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4.941642</td>\n",
       "      <td>9.087834</td>\n",
       "      <td>8.248791</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>6.967909</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.950323</td>\n",
       "      <td>10.732651</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>10.095388</td>\n",
       "      <td>7.260523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.335634</td>\n",
       "      <td>8.911530</td>\n",
       "      <td>5.164786</td>\n",
       "      <td>8.151333</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
       "      <td>7.036148</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>8.390949</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>6.882437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh      Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "128  4.941642  9.087834   8.248791  4.955827          6.967909      1.098612\n",
       "154  6.432940  4.007333   4.919981  4.317488          1.945910      2.079442\n",
       "65   4.442651  9.950323  10.732651  3.583519         10.095388      7.260523\n",
       "66   2.197225  7.335634   8.911530  5.164786          8.151333      3.295837\n",
       "75   9.923192  7.036148   1.098612  8.390949          1.098612      6.882437"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_dict = {}\n",
    "# 对于每一个特征，找到值异常高或者是异常低的数据点\n",
    "for feature in log_data.keys():\n",
    "    \n",
    "    # TODO：计算给定特征的Q1（数据的25th分位点）\n",
    "    Q1 = np.percentile(a=log_data[feature], q=25)\n",
    "    \n",
    "    # TODO：计算给定特征的Q3（数据的75th分位点）\n",
    "    Q3 = np.percentile(a=log_data[feature], q=75)\n",
    "    \n",
    "    # TODO：使用四分位范围计算异常阶（1.5倍的四分位距）\n",
    "    step = 1.5 * (Q3 - Q1)\n",
    "    \n",
    "    # 显示异常点\n",
    "    print \"Data points considered outliers for the feature '{}':\".format(feature)\n",
    "    outlier_candidate = log_data[(log_data[feature] < Q1 - step) | (log_data[feature] > Q3 + step)]\n",
    "    idx_dict[feature] = list(outlier_candidate.index)\n",
    "    print idx_dict[feature]\n",
    "#     display(outlier_candidate.sort_values(feature))\n",
    "\n",
    "# 统计一下出现在多个特征的异常值范围的记录\n",
    "freq_map = {}\n",
    "for f in idx_dict.values():\n",
    "    for x in f:\n",
    "        freq_map.setdefault(x, 0)\n",
    "        freq_map[x] += 1\n",
    "\n",
    "# 显示出现频率多于1次的记录编号\n",
    "\n",
    "print '\\nSamples categorized as Outlier for more than once:'\n",
    "for x in freq_map:\n",
    "    if freq_map[x] > 1:\n",
    "        print x, '->', freq_map[x], 'times'\n",
    "    \n",
    "# 可选：选择你希望移除的数据点的索引\n",
    "# 具体移除原因见后面的分析\n",
    "outliers  = [128, 154, 65, 66, 75]\n",
    "\n",
    "# 如果选择了的话，移除异常点\n",
    "good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)\n",
    "print '\\nSample Size shrink from {} to {}.'.format(log_data.shape[0], good_data.shape[0])\n",
    "\n",
    "print '\\nHere are the outliers:'\n",
    "log_data.loc[outliers]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 4\n",
    "*请列出所有在多于一个特征下被看作是异常的数据点。这些点应该被从数据集中移除吗？为什么？把你认为需要移除的数据点全部加入到到`outliers`变量中。* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Review Comments\n",
    "\n",
    "异常点的寻找和判断是否移除是一个复杂的事情，这里会融入比较多的 domain knowledge，Tukey的定义异常值的方法只是方法之一。有时也要根据模型的特点来决定。例如有的模型对异常点非常敏感，异常点的存在会影响模型的预测结果；有的模型对异常点不那么敏感。更多时候会通过比较移除异常点和不移除异常点的预测结果来决定是否应该做这件事。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**原回答:**\n",
    "\n",
    "上面的代码已经统计了在所有特征下被判断为异常值的记录编号，并统计得到出现频率大于一次的记录有5个，分别是[128, 154, 65, 66, 75].\n",
    "\n",
    "之前挑选的三个Sample中有一个就在这个范围内...因此仅将剩下的4个点从log_data中去除。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rework Answer\n",
    "\n",
    "这次在前几个问题中我已经重新选取了sample，没有再选属于这5个异常点之中的点（65），而是选了一个类似分布但是没有那么异常的点，因此这里移除异常点的时候不会受限制了。下面开始分析为什么移除异常点：\n",
    "\n",
    "- 如何判断Outlier是否应该去除？要看移除先后对模型产生的影响有多大。然而模型最终的目的是为了发掘隐藏的真相，或者用于预测新的数据。因此异常点的影响应该具体到是否会导致聚类划分准确度下降这类问题上。\n",
    "\n",
    "\n",
    "- 如何衡量对模型的影响大小？感觉应该用后面的轮廓系数衡量聚类的好坏，或者直接比对原有Channel的预测正确率。\n",
    "\n",
    "\n",
    "- 下面我用了后面问题所采用的方法对移除异常点前后的模型性能进行了分析。\n",
    "\n",
    "（其实我觉得这个问题应该在项目最后问。要不然一路从问题1做到这的时候，手上的工具其实很少，有些无从下手啊!!!）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 通过移除Tukey定义的异常点，对比K-Means与GMM对异常点的敏感性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Using KMeans Model\n",
      "Before Remove Outliers: Cluster Accuracy = 0.951724137931, Silhouette Score = 0.419166083203\n",
      " After Remove Outliers: Cluster Accuracy = 0.95632183908, Silhouette Score = 0.426281015469\n",
      "\n",
      "Using GMM Model\n",
      "Before Remove Outliers: Cluster Accuracy = 0.87816091954, Silhouette Score = 0.409968324528\n",
      " After Remove Outliers: Cluster Accuracy = 0.91724137931, Silhouette Score = 0.421916846463\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "candidates = [128, 154, 65, 66, 75]\n",
    "# cluster.csv already removed the 5 candidate outliers. (Shape = 435)\n",
    "y_true = pd.read_csv('cluster.csv')['cluster']\n",
    "# customers.csv has the 5 candidate outliers. (Shape = 440)\n",
    "before_removal = log_data\n",
    "after_removal = log_data.drop(log_data.index[candidates]).reset_index(drop = True)\n",
    "\n",
    "def test_outliers(model, data, components):\n",
    "    pca = PCA(n_components=components, random_state=0)\n",
    "    pca.fit(data)\n",
    "    data = pca.transform(data)\n",
    "    data = pd.DataFrame(data)\n",
    "    if model is 'KMeans':\n",
    "        clusterer = KMeans(n_clusters=components, max_iter=500, n_init=10, random_state=0)\n",
    "    else:\n",
    "        clusterer = GaussianMixture(n_components=components, random_state=0)\n",
    "    clusterer.fit(data)\n",
    "    preds = clusterer.predict(data)\n",
    "    score = silhouette_score(data, preds)\n",
    "    return preds, score\n",
    "\n",
    "def compare_model(model):\n",
    "    print '\\nUsing {} Model'.format(model)\n",
    "    y_pred_before, score = test_outliers(model, before_removal, 2)\n",
    "    y_pred_before = pd.Series(y_pred_before).drop(log_data.index[candidates]).reset_index(drop = True)\n",
    "    acc_score = accuracy_score(y_true, y_pred_before)\n",
    "    # Swap label's 0/1 to get the max accuray score.\n",
    "    acc_score = max(acc_score, 1 - acc_score)\n",
    "    print 'Before Remove Outliers: Cluster Accuracy = {}, Silhouette Score = {}'.format(acc_score, score)\n",
    "    y_pred_after, score = test_outliers(model, after_removal, 2)\n",
    "    acc_score = accuracy_score(y_true, y_pred_after)\n",
    "    acc_score = max(acc_score, 1 - acc_score)\n",
    "    print ' After Remove Outliers: Cluster Accuracy = {}, Silhouette Score = {}'.format(acc_score, score)\n",
    "\n",
    "compare_model('KMeans')\n",
    "compare_model('GMM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 通过上面的计算可以看到，移除同样的5个异常点，K-Means预测的准确率上升了0.5%，而GMM则上升了4%，这起码说明GMM模型对于这5个异常点的敏感度要远远超过K-Means。虽然实际生活中可能不会刚好有现成的label供你评估聚类的准确性，但是在这里也不失为一种评估异常点对模型影响大小的方法。\n",
    "\n",
    "\n",
    "- 原本我还想通过比对移除异常点前后的轮廓系数来说明问题，但是考虑到轮廓系数仅仅是每个点距离自己所属聚类中心和最近其他聚类中心的比例，这个参数顶多能够分析聚类的离散程度，但是聚类离散并不是聚类的错，而仅仅是一种数据的分布特征而已，显然越致密、相互分的越开的聚类轮廓系数得分越高，但这并不能够证明异常点对模型的影响力，因此轮廓系数不能作为移除异常点的依据。\n",
    "\n",
    "\n",
    "- 结论：由于上面的计算显示移除异常点后的确能够提高模型的预测准确性（在具有Ground Truth的前提下），因此这里选择移除了这5个异常点。\n",
    "\n",
    "\n",
    "- 其实关于Outlier选取主要还是要靠实验。http://scikit-learn.org/stable/modules/outlier_detection.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征转换\n",
    "在这个部分中你将使用主成分分析（PCA）来分析批发商客户数据的内在结构。由于使用PCA在一个数据集上会计算出最大化方差的维度，我们将找出哪一个特征组合能够最好的描绘客户。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习: 主成分分析（PCA）\n",
    "\n",
    "既然数据被缩放到一个更加正态分布的范围中并且我们也移除了需要移除的异常点，我们现在就能够在`good_data`上使用PCA算法以发现数据的哪一个维度能够最大化特征的方差。除了找到这些维度，PCA也将报告每一个维度的*解释方差比（explained variance ratio）*--这个数据有多少方差能够用这个单独的维度来解释。注意PCA的一个组成部分（维度）能够被看做这个空间中的一个新的“特征”，但是它是原来数据中的特征构成的。\n",
    "\n",
    "在下面的代码单元中，你将要实现下面的功能：\n",
    " - 导入`sklearn.decomposition.PCA`并且将`good_data`用PCA并且使用6个维度进行拟合后的结果保存到`pca`中。\n",
    " - 使用`pca.transform`将`log_samples`进行转换，并将结果存储到`pca_samples`中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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atIg5c+bQsWPHousNGTKEXr16AfD0009z6KGH0qlTJzp16sQhhxwCwOLFi3nm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\nJEmSJHUwMEmSJElSBwOTJEmSJHX4A2OpVupRACxiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2d5d2970>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO：通过在good data上使用PCA，将其转换成和当前特征数一样多的维度\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=6, random_state=0)\n",
    "pca.fit(good_data)\n",
    "\n",
    "# TODO：使用上面的PCA拟合将变换施加在log_samples上\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# 生成PCA的结果图\n",
    "pca_results = vs.pca_results(good_data, pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 5\n",
    "*数据的第一个和第二个主成分* **总共** *表示了多少的方差？*  前四个主成分呢？使用上面提供的可视化图像，讨论从用户花费的角度来看前四个**主要成分**的消费行为最能代表哪种类型的客户并给出你做出判断的理由。\n",
    "\n",
    "**提示：** 某一特定维度上的正向增长对应**正权**特征的**增长**和**负权**特征的**减少**。增长和减少的速率和每个特征的权重相关。[参考资料(英文)](https://onlinecourses.science.psu.edu/stat505/node/54)。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答:**\n",
    "\n",
    "前2个主成分包含了0.71的方差。前4个主成分则包含了0.93的方差（几乎是全部了），说明经过PCA变换后的特征只需要用4个新特征几乎就能代替原来的6个特征了。\n",
    "\n",
    "- 第0个主成分中：Milk + Grocery + Detergents_Paper 一伙，与 Fresh + Frozen 呈负相关。这应该是**光卖牛奶和非食物，或者卖食物就不卖牛奶和非食物的行为特质**。\n",
    "- 第1个主成分中：Fresh + Frozen + Deli 一伙，与其他特征全都呈正相关。这应该是**什么都卖，而且主要卖食品的行为特质**。\n",
    "- 第2个主成分中：Fresh + Detergents_Paper 一伙，与 Deli + Frozen 呈强烈负相关，这应该是**卖熟食和冷冻食品，但是不卖生鲜的行为特质**。\n",
    "- 第3个主成分中：Deli + Fresh 一伙，与 Frozen 呈强烈负相关，这应该是**卖熟食和生鲜，但是不卖冷冻食品的行为特质**（和上面的组合不同）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征 to 主成分的具体投影关系：以第一维度为例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xL9ZYeWidEkmSJEmds+hJS+jJ7Qwe/fZ+DB4UdUgjSZIk1c6ipwErM9ng+Mtr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\n7KF4oc09AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f6b89d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights for PC#0:  [ 0.1675 -0.4014 -0.4381  0.1782 -0.7514 -0.1499]\n"
     ]
    }
   ],
   "source": [
    "# 6 Features Project to Dimension 1 (PC #0)\n",
    "# coef 就是上图中每个Bar在纵坐标上的值（权重系数）\n",
    "coef = pca.components_[0, :]\n",
    "test_feature = np.dot(log_data, coef).T            # test_feature 是所有特征与相应权重的组合值\n",
    "test_pca = pd.DataFrame(pca.transform(log_data))\n",
    "test_pca = list(test_pca.loc[:, 0])\n",
    "\n",
    "plt.figure(figsize=(15,3))\n",
    "plt.plot(test_feature, test_pca)\n",
    "plt.title(\"6 Features to PC#0 Projection\")\n",
    "plt.xlabel('All 6 Features')\n",
    "plt.ylabel('PC#0')\n",
    "plt.show()\n",
    "print 'Weights for PC#0: ', coef.round(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，原来的6个特征向第0个主成分的投影是完全线性的，说明sklearn.pca求得的主成分的确是所有特征的线性组合，斜率是对所有系数的放大倍数（因为之前计算出的权重系数已经统一化简至1以内），截距是一个常数。因此可以直观的显示为如下的方程：\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "&Dimension_{\\,0} = k \\cdot (w_0 \\cdot \\mathrm{Fresh} - w_1 \\cdot \\mathrm{Milk} - w_2 \\cdot \\mathrm{Grocery} + w_3 \\cdot \\mathrm{Frozen} - w_4 \\cdot \\mathrm{Dp} - w_5 \\cdot \\mathrm{Deli}) + intercept\\\\[2ex]\n",
    "&\\mathbf{w} = [0.16, \\;-0.40, \\;-0.43, \\;0.17, \\;-0.75, \\;-0.14]\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "总结下，主成分的计算可以理解为：\n",
    "    - 矩阵的线性变换\n",
    "    - 坐标轴的投影\n",
    "    \n",
    "现在我理解上面的6个直方图其实呈现的是特征到主成分的映射关系，以及在一个主成分中，这些特征相互之间是如何相关的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 观察\n",
    "运行下面的代码，查看经过对数转换的样本数据在进行一个6个维度的主成分分析（PCA）之后会如何改变。观察样本数据的前四个维度的数值。考虑这和你初始对样本点的解释是否一致。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "      <th>Dimension 3</th>\n",
       "      <th>Dimension 4</th>\n",
       "      <th>Dimension 5</th>\n",
       "      <th>Dimension 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.3646</td>\n",
       "      <td>-3.9519</td>\n",
       "      <td>-0.1229</td>\n",
       "      <td>0.6240</td>\n",
       "      <td>0.5379</td>\n",
       "      <td>0.0551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.3971</td>\n",
       "      <td>5.6303</td>\n",
       "      <td>1.9179</td>\n",
       "      <td>0.3527</td>\n",
       "      <td>-0.4430</td>\n",
       "      <td>0.0791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0206</td>\n",
       "      <td>4.8169</td>\n",
       "      <td>6.4519</td>\n",
       "      <td>2.7403</td>\n",
       "      <td>0.7788</td>\n",
       "      <td>2.1415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2  Dimension 3  Dimension 4  Dimension 5  \\\n",
       "0      -4.3646      -3.9519      -0.1229       0.6240       0.5379   \n",
       "1      -2.3971       5.6303       1.9179       0.3527      -0.4430   \n",
       "2       3.0206       4.8169       6.4519       2.7403       0.7788   \n",
       "\n",
       "   Dimension 6  \n",
       "0       0.0551  \n",
       "1       0.0791  \n",
       "2       2.1415  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示经过PCA转换的sample log-data\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习：降维\n",
    "当使用主成分分析的时候，一个主要的目的是减少数据的维度，这实际上降低了问题的复杂度。当然降维也是需要一定代价的：更少的维度能够表示的数据中的总方差更少。因为这个，*累计解释方差比（cumulative explained variance ratio）*对于我们确定这个问题需要多少维度非常重要。另外，如果大部分的方差都能够通过两个或者是三个维度进行表示的话，降维之后的数据能够被可视化。\n",
    "\n",
    "在下面的代码单元中，你将实现下面的功能：\n",
    " - 将`good_data`用两个维度的PCA进行拟合，并将结果存储到`pca`中去。\n",
    " - 使用`pca.transform`将`good_data`进行转换，并将结果存储在`reduced_data`中。\n",
    " - 使用`pca.transform`将`log_samples`进行转换，并将结果存储在`pca_samples`中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO：通过在good data上进行PCA，将其转换成两个维度\n",
    "# (不要开启Whiten！否则后续KMeans聚类的最佳划分是3个！)\n",
    "pca = PCA(n_components=2, random_state=0)\n",
    "pca.fit(good_data)\n",
    "\n",
    "# TODO：使用上面训练的PCA将good data进行转换\n",
    "reduced_data = pca.transform(good_data)\n",
    "\n",
    "# TODO：使用上面训练的PCA将log_samples进行转换\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# 为降维后的数据创建一个DataFrame\n",
    "reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 观察\n",
    "运行以下代码观察当仅仅使用两个维度进行PCA转换后，这个对数样本数据将怎样变化。观察这里的结果与一个使用六个维度的PCA转换相比较时，前两维的数值是保持不变的。\n",
    "\n",
    "#### 每个主成分都包含所有特征，因此不管PCA分离出多少个主成分，最重要的主成分的权重系数一定是一样的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.3646</td>\n",
       "      <td>-3.9519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.3971</td>\n",
       "      <td>5.6303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0206</td>\n",
       "      <td>4.8169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2\n",
       "0      -4.3646      -3.9519\n",
       "1      -2.3971       5.6303\n",
       "2       3.0206       4.8169"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 展示经过两个维度的PCA转换之后的样本log-data\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可视化一个双标图（Biplot）\n",
    "双标图是一个散点图，每个数据点的位置由它所在主成分的分数确定。坐标系是主成分（这里是`Dimension 1` 和 `Dimension 2`）。此外，双标图还展示出初始特征在主成分上的投影。一个双标图可以帮助我们理解降维后的数据，发现主成分和初始特征之间的关系。\n",
    "\n",
    "运行下面的代码来创建一个降维后数据的双标图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f819670>"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fI9Bzli27iGLODqtNREQhEZ1Q+M3GmL6oy0LVEZE+aHB3qzHm01GXhxwicgV0/sUlRfrI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\n9Ti6997yAi3v8djcrFmbmTOd49EvQ9LUpOfG7Nn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1IyJXAbgKABYtCjOepDSqZVOQOPcd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f6e6e70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a biplot\n",
    "vs.biplot(good_data, reduced_data, pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 观察\n",
    "\n",
    "一旦我们有了原始特征的投影（红色箭头），就能更加容易的理解散点图每个数据点的相对位置。\n",
    "\n",
    "在这个双标图中，哪些初始特征与第一个主成分有强关联？哪些初始特征与第二个主成分相关联？你观察到的是否与之前得到的 pca_results 图相符？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "答：从图中可以看到，\n",
    "\n",
    "- 第一维度上主要集中了曾经的  Fresh / Frozen / Deli 这三个特征，\n",
    "- 第二维度上则集中了 Milk / Grocery / Detergents_Paper 这三个特征。\n",
    "\n",
    "这与pca成分图里的前两个主成分中的特征占比图吻合（下面是之前分析的每个主成分的构成方式）\n",
    "\n",
    "- 第0个主成分中：Milk + Grocery + Detergents_Paper 一伙，与 Fresh + Frozen 呈负相关。\n",
    "- 第1个主成分中：Fresh + Frozen + Deli 一伙，与其他特征全都呈正相关。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 聚类\n",
    "\n",
    "在这个部分，你讲选择使用K-Means聚类算法或者是高斯混合模型聚类算法以发现数据中隐藏的客户分类。然后，你将从簇中恢复一些特定的关键数据点，通过将它们转换回原始的维度和规模，从而理解他们的含义。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 6\n",
    "*使用K-Means聚类算法的优点是什么？使用高斯混合模型聚类算法的优点是什么？基于你现在对客户数据的观察结果，你选用了这两个算法中的哪一个，为什么？*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答:**\n",
    "\n",
    "### K-Means 优点\n",
    "\n",
    "- 当样本量很庞大时，K-Means算法的时间复杂度随是样本数量线性增长 $O(N)$，快速而高效。\n",
    "\n",
    "### K-Means 缺点\n",
    "\n",
    "- 初始点位置的选取会极大的影响聚类结果，因此需要多次启动求平均\n",
    "- 聚类个数$K$的选取原则并不明确\n",
    "- 对outlier敏感\n",
    "- 对于混叠严重的数据分类效果不好\n",
    "- 聚类的形状只能是球状\n",
    "\n",
    "### GMM 聚类的优点\n",
    "\n",
    "- 基于概率分布，因此判决是软判决，对于混叠较严重的聚类有更好的分类效果。\n",
    "- 聚类的形状不局限于球型，可以扭曲为椭球型。\n",
    "\n",
    "### GMM 聚类的缺点\n",
    "\n",
    "- 同样需要面对初始点选取问题和聚类个数确定问题。\n",
    "- 实际上，K-Means可以理解为GMM的特殊形式，即判决为100%的情形。因此K-Means的一些缺点GMM依然有。\n",
    "\n",
    "按理说GMM因为使用了软判决，应该会被K-Means好一些，但是也得根据具体数据集的特点来决定。实践出真知，本着实验的目的，我两个算法都用了下，具体结果见后面的计算。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习: 创建聚类\n",
    "\n",
    "针对不同情况，有些问题你需要的聚类数目可能是已知的。但是在聚类数目不作为一个**先验**知道的情况下，我们并不能够保证某个聚类的数目对这个数据是最优的，因为我们对于数据的结构（如果存在的话）是不清楚的。但是，我们可以通过计算每一个簇中点的**轮廓系数**来衡量聚类的质量。数据点的[轮廓系数](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html)衡量了它与分配给他的簇的相似度，这个值范围在-1（不相似）到1（相似）。**平均**轮廓系数为我们提供了一种简单地度量聚类质量的方法。\n",
    "\n",
    "$$\n",
    "\\displaystyle\n",
    "\\begin{aligned}\n",
    "&a = \\mathbf{Distance}(\\mathrm{sample, \\; predicted\\_cluster\\_center})\\\\[2ex]\n",
    "&b = \\mathbf{Distance}(\\mathrm{sample, \\; other\\_nearest\\_cluster\\_center})\\\\[2ex]\n",
    "&\\mathbf{Silhouette \\; Score_{\\; Sample}} = \\frac {b - a}{\\max(a, b)}\\\\[2ex]\n",
    "&\\mathbf{Silhouette \\; Score_{\\; Mean}} = \\frac{1}{n} \\cdot \\sum_{i=1}^n \\frac {b_i - a_i}{\\max(a_i, b_i)} \\quad \\in [-1, 1]\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "- $b-a$ 通过符号可以判断该Sample点离所分配的聚类中心近还是别的聚类中心近。\n",
    "- $\\max(a,b)$ 用于将系数缩放至单位1范围内。\n",
    "\n",
    "\n",
    "在接下来的代码单元中，你将实现下列功能：\n",
    " - 在`reduced_data`上使用一个聚类算法，并将结果赋值到`clusterer`，需要设置 `random_state` 使得结果可以复现。\n",
    " - 使用`clusterer.predict`预测`reduced_data`中的每一个点的簇，并将结果赋值到`preds`。\n",
    " - 使用算法的某个属性值找到聚类中心，并将它们赋值到`centers`。\n",
    " - 预测`pca_samples`中的每一个样本点的类别并将结果赋值到`sample_preds`。\n",
    " - 导入sklearn.metrics.silhouette_score包并计算`reduced_data`相对于`preds`的轮廓系数。\n",
    "   - 将轮廓系数赋值给`score`并输出结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用K-Means进行聚类划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.426281015469\n"
     ]
    }
   ],
   "source": [
    "# TODO：在降维后的数据上使用你选择的聚类算法\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "clusterer = KMeans(n_clusters=2, max_iter=500, n_init=10, random_state=0)\n",
    "clusterer.fit(reduced_data)\n",
    "\n",
    "# TODO：预测每一个点的簇\n",
    "preds = clusterer.predict(reduced_data)    # preds 是每一条记录的所属类编号号，取值范围为[0, n_clusters - 1]\n",
    "\n",
    "# TODO：找到聚类中心\n",
    "centers = clusterer.cluster_centers_\n",
    "\n",
    "# TODO：预测在每一个转换后的样本点的类\n",
    "sample_preds = clusterer.predict(pca_samples)\n",
    "\n",
    "# TODO：计算选择的类别的平均轮廓系数（mean silhouette coefficient）\n",
    "score = silhouette_score(reduced_data, preds)\n",
    "print score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.49093664 -0.10743169]\n",
      " [-2.17322969  0.15659534]]\n"
     ]
    }
   ],
   "source": [
    "print clusterer.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VQamzhkbEEHAF8GrgAeDGiFiTmbdNqrc/cB7wzbriHwCvzcytEXE4sA5wCk5JkiRJ2kNl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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2efb2f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 查看聚类个数与轮廓系数之间的关系\n",
    "def cluster_score_relation(max_clusters):\n",
    "    stats = []\n",
    "    for c in range(2, max_clusters + 1):\n",
    "        clusterer = KMeans(n_clusters=c, max_iter=100, n_init=10, random_state=0)\n",
    "        clusterer.fit(reduced_data)\n",
    "        preds = clusterer.predict(reduced_data)\n",
    "        score = silhouette_score(reduced_data, preds)\n",
    "        stats.append(score)\n",
    "#         print 'Cluster {} has Silhouette Score {}'.format(c, score)\n",
    "    return stats\n",
    "\n",
    "search_range = 30\n",
    "stats = cluster_score_relation(search_range)\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.title(\"$\\mathbf{K-Means}$: Relationship between n_cluster and silhouette_score\")\n",
    "plt.scatter(range(2, search_range + 1), stats)\n",
    "plt.grid(True)\n",
    "plt.xlabel('n_cluster')\n",
    "plt.ylabel('silhouette_score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用高斯混合模型进行聚类划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hxm2jbely6DWOVSaNhME9La9CkiRJqqHdFzD3Gscqi0bC4FYq3URjiucSOH5W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\nAgVKQ3pezA3CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f64e790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "\n",
    "# 查看聚类个数与轮廓系数之间的关系\n",
    "def cluster_score_relation(max_clusters):\n",
    "    stats = []\n",
    "    for c in range(2, max_clusters + 1):\n",
    "        clusterer = GaussianMixture(n_components=c, random_state=0)\n",
    "        clusterer.fit(reduced_data)\n",
    "        preds = clusterer.predict(reduced_data)\n",
    "        score = silhouette_score(reduced_data, preds)\n",
    "        stats.append(score)\n",
    "#         print '{} Cluster has Silhouette Score {}'.format(c, score)\n",
    "    return stats\n",
    "\n",
    "search_range = 30\n",
    "stats = cluster_score_relation(search_range)\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.title(\"$\\mathbf{GMM}$: Relationship between n_cluster and silhouette_score\")\n",
    "plt.scatter(range(2, search_range + 1), stats)\n",
    "plt.grid(True)\n",
    "plt.xlabel('n_cluster')\n",
    "plt.ylabel('silhouette_score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 7\n",
    "\n",
    "*汇报你尝试的不同的聚类数对应的轮廓系数。在这些当中哪一个聚类的数目能够得到最佳的轮廓系数？* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答:** 2个。\n",
    "\n",
    "分别使用K-Means和GMM进行聚类划分，为了确定最佳聚类个数，对n_clusters进行参数搜索，计算聚类个数从2至30时的对应轮廓系数值，将两者之间的关系通过画图呈现出来（见上面的Cell）。\n",
    "\n",
    "可以看到，达到全局最高值（0.426）的聚类个数是2.\n",
    "\n",
    "n_clusters | silhouette_score\n",
    "---  | ---\n",
    "2    | 0.426\n",
    "3    | 0.393\n",
    "4    | 0.332\n",
    "...  | ...\n",
    "\n",
    "GMM得到的最佳聚类划分个数与K-Means一致，都是2个。平均轮廓系数也十分相近，K-Means为0.426，GMM则是0.424，可以说差距十分微小。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 聚类可视化\n",
    "一旦你选好了通过上面的评价函数得到的算法的最佳聚类数目，你就能够通过使用下面的代码块可视化来得到的结果。作为实验，你可以试着调整你的聚类算法的聚类的数量来看一下不同的可视化结果。但是你提供的最终的可视化图像必须和你选择的最优聚类数目一致。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HHKAgKA9mxgEHHFBQb5oCIRERERGRClEQlL9Cj50CIRERERGROvXiiy8yc+ZM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\nXy8r6dHd6Y+Zh7CiZk5XeKEZlvj3B4dIn6kVqZ8+rDkbztb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P99SMiD+9bs069/0Xvlxc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f49dd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从已有的实现中展示聚类的结果\n",
    "vs.cluster_results(reduced_data, preds, centers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 练习: 数据恢复\n",
    "上面的可视化图像中提供的每一个聚类都有一个中心点。这些中心（或者叫平均点）并不是数据中真实存在的点，但是是所有预测在这个簇中的数据点的*平均*。对于创建客户分类的问题，一个簇的中心对应于*那个分类的平均用户*。因为这个数据现在进行了降维并缩放到一定的范围，我们可以通过施加一个反向的转换恢复这个点所代表的用户的花费。\n",
    "\n",
    "在下面的代码单元中，你将实现下列的功能：\n",
    " - 使用`pca.inverse_transform`将`centers` 反向转换，并将结果存储在`log_centers`中。\n",
    " - 使用`np.log`的反函数`np.exp`反向转换`log_centers`并将结果存储到`true_centers`中。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Segment 0</th>\n",
       "      <td>8867.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>2477.0</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>4005.0</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>12104.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>4561.0</td>\n",
       "      <td>1036.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0  8867.0  1897.0   2477.0  2088.0             294.0         681.0\n",
       "Segment 1  4005.0  7900.0  12104.0   952.0            4561.0        1036.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO：反向转换中心点\n",
    "log_centers = pca.inverse_transform(centers)\n",
    "\n",
    "# TODO：对中心点做指数转换\n",
    "true_centers = np.exp(log_centers)\n",
    "\n",
    "# 显示真实的中心点(给true_centers加上列名和Index名)\n",
    "segments = ['Segment {}'.format(i) for i in range(0,len(centers))]\n",
    "true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys())\n",
    "true_centers.index = segments\n",
    "display(true_centers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 8\n",
    "考虑上面的代表性数据点在每一个产品类型的花费总数，*你认为这些客户分类代表了哪类客户？为什么？*需要参考在项目最开始得到的统计值来给出理由。\n",
    "\n",
    "**提示：** 一个被分到`'Cluster X'`的客户最好被用 `'Segment X'`中的特征集来标识的企业类型表示。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 两类客户细分的典型特征数值对比整体均值（需求量最大的特征用粗体标注）\n",
    "\n",
    "Data  |Fresh | Milk | Grocery | Frozen | Deter_Paper | Deli\n",
    "---   |---   | ---  | ---     | ---    | ---              | ---\n",
    "**Segment 0** |\t$\\color{red} {9041}$  |\t1924 |\t2508 | \t2116.0\t| 301.0\t| 689.0\n",
    "**Segment 1** |\t3754\t|7970\t| $\\color{red} {12296}$\t|904\t| 4666\t|1027\n",
    "**Average**   | 12000   | 5769  | 7951  | 3071  | 2881  | 1524"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答:**\n",
    "\n",
    "- Segment 0的典型客户主要需要Fresh，辅助以少量Grocery/Frozen/Milk，几乎不需要Detergents_Paper和Deli。主营生鲜。\n",
    "\n",
    "- Segment 1的典型客户主要需要Grocery，辅助以远超平均值的大量Milk/Detergents_Paper/Fresh，对Frozen和Deli需求较少。主营非食物。\n",
    "\n",
    "进一步分析有以下结论：\n",
    "\n",
    "- Segment 0各方面的购入金额都偏小(相比于数据集平均值而言)，主营Fresh，以及少量其他食品类型，因此应该是**食品专卖零售店企业**。\n",
    "\n",
    "- Segment 1主要需求是非食物。通常来讲对各项杂物和清洁用品需求量大的企业通常是服务行业，例如酒店。考虑到Milk和Fresh也有一定购入，因此也涉及到咖啡馆等简食行业。综合起来，Segment1应该是提供咖啡馆或者in-room dining的**酒店/餐饮企业**。\n",
    "\n",
    "\n",
    "结合之前对于主成分的分析可以看到，**主成分并不能代表一类客户，而仅仅是客户的一类行为特质而已**。\n",
    "- 第0个主成分中：Milk + Grocery + Detergents_Paper 一伙。这应该是光卖牛奶和非食物，或者卖食物就不卖牛奶和非食物的行为特质。\n",
    "- 第1个主成分中：Fresh + Frozen + Deli 一伙，与其他特征全都呈正相关。这应该是什么都卖，而且主要卖食品的行为特质。\n",
    "- 第2个主成分中：Fresh + Detergents_Paper 一伙，与 Deli + Frozen 呈强烈负相关，这应该是卖熟食和冷冻食品，但是不卖生鲜的行为特质。\n",
    "- 第3个主成分中：Deli + Fresh 一伙，与 Frozen 呈强烈负相关，这应该是卖熟食和生鲜，但是不卖冷冻食品的行为特质（和上面的组合不同）。\n",
    "\n",
    "第一主成分Detergents_Paper、Grocery、Milk有较高的比重，说明同时购买的可能性较大。后三个主成分有一定的相关性，第二主成分Fresh、Frozen、Delicatessen较大，同时购买的可能性较大；第三主成分说明在第二成分的基础上购买Frozen、Delicatessen但不买Fresh的可能性加大；第四主成分说明购买Frozen但不购买Delicatessen的可能性加大。**所以通过第一主成分，第二三四主成分能明显区别两类用户**。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 9\n",
    "*对于每一个样本点 * **问题 8**  *中的哪一个分类能够最好的表示它？你之前对样本的预测和现在的结果相符吗？*\n",
    "\n",
    "运行下面的代码单元以找到每一个样本点被预测到哪一个簇中去。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44466</td>\n",
       "      <td>54259</td>\n",
       "      <td>55571</td>\n",
       "      <td>7782</td>\n",
       "      <td>24171</td>\n",
       "      <td>6465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>2616</td>\n",
       "      <td>8118</td>\n",
       "      <td>145</td>\n",
       "      <td>3874</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>333</td>\n",
       "      <td>7021</td>\n",
       "      <td>15601</td>\n",
       "      <td>15</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0  44466  54259    55571    7782             24171          6465\n",
       "1     23   2616     8118     145              3874           217\n",
       "2      3    333     7021   15601                15           550"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample point 0 predicted to be in Cluster 1\n",
      "Sample point 1 predicted to be in Cluster 1\n",
      "Sample point 2 predicted to be in Cluster 0\n"
     ]
    }
   ],
   "source": [
    "# 显示预测结果\n",
    "for i, pred in enumerate(sample_preds):\n",
    "    print \"Sample point\", i, \"predicted to be in Cluster\", pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答:**\n",
    "\n",
    "计算结果显示，前两个样本都被归为了酒店/餐饮企业，而最后一个样本被归为零售企业。\n",
    "\n",
    "我最初分析的结果是，第一个样本属于大型超市，第二个样本属于咖啡馆，第三个样本属于零售店。由于第一个样本各方面数值都很庞大，因此如果属于万豪这种大型连锁酒店来说，也是合理的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结论\n",
    "\n",
    "在最后一部分中，你要学习如何使用已经被分类的数据。首先，你要考虑不同组的客户**客户分类**，针对不同的派送策略受到的影响会有什么不同。其次，你要考虑到，每一个客户都被打上了标签（客户属于哪一个分类）可以给客户数据提供一个多一个特征。最后，你会把客户分类与一个数据中的隐藏变量做比较，看一下这个分类是否辨识了特定的关系。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 问题 10\n",
    "在对他们的服务或者是产品做细微的改变的时候，公司经常会使用[A/B tests](https://en.wikipedia.org/wiki/A/B_testing)以确定这些改变会对客户产生积极作用还是消极作用。这个批发商希望考虑将他的派送服务从每周5天变为每周3天，但是他只会对他客户当中对此有积极反馈的客户采用。*这个批发商应该如何利用客户分类来知道哪些客户对它的这个派送策略的改变有积极的反馈，如果有的话？你需要给出在这个情形下A/B 测试具体的实现方法，以及最终得出结论的依据是什么？*  \n",
    "**提示：** 我们能假设这个改变对所有的客户影响都一致吗？我们怎样才能够确定它对于哪个类型的客户影响最大？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：**\n",
    "\n",
    "由于目前客户只分为两类：食品零售商，以及酒店/餐馆等服务性企业。派送的频率降低，会如何对这两类企业产生积极或消极的影响呢？\n",
    "\n",
    "由于派送频率降低对两类客户的影响很有可能不同，因此为了确定影响各是什么样的，我们需要对两类企业分别随机抽样，从食品零售商大类中挑选50%的企业采用新的配送方案（称为方案B），另50%保持不变（称为方案A）。然后对酒店/餐饮这类企业中做同样的事情。\n",
    "\n",
    "在实施A/B测试一段时间以后，收集所有客户的反馈。针对每类客户，统计到底方案A还是方案B的积极反馈多。如果方案B的积极反馈远远大于方案A，那么最后就会推广方案B。\n",
    "\n",
    "其实严谨的讲应该具体分析积极反馈和消极反馈的占比，有可能需要在每类企业内再按某些因素进一步细分才能做到真正精确，毕竟目前划分的这两大类还相当宽泛。酒店和饭馆对于进货的频次需求肯定是不同的，这需要结合领域知识进一步分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 11\n",
    "通过聚类技术，我们能够将原有的没有标记的数据集中的附加结构分析出来。因为每一个客户都有一个最佳的划分（取决于你选择使用的聚类算法），我们可以把*用户分类*作为数据的一个[**工程特征**](https://en.wikipedia.org/wiki/Feature_learning#Unsupervised_feature_learning)。假设批发商最近迎来十位新顾客，并且他已经为每位顾客每个产品类别年度采购额进行了预估。进行了这些估算之后，批发商该如何运用它的预估和**非监督学习的结果**来对这十个新的客户进行更好的预测？\n",
    "\n",
    "**提示：**在下面的代码单元中，我们提供了一个已经做好聚类的数据（聚类结果为数据中的cluster属性），我们将在这个数据集上做一个小实验。尝试运行下面的代码看看我们尝试预测‘Region’的时候，如果存在聚类特征'cluster'与不存在相比对最终的得分会有什么影响？这对你有什么启发？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **CHANNEL**: customer Channel - HoReCa (Hotel/Restaurant/Cafe) or Retail channel (Nominal) \n",
    "- **REGION**: customers Region - Lisnon, Oporto or Other (Nominal) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "# 读取包含聚类结果的数据\n",
    "cluster_data = pd.read_csv(\"cluster.csv\")\n",
    "y = cluster_data['Region']\n",
    "X = cluster_data.drop(['Region'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen  \\\n",
       "0       3  12669  9656     7561     214              2674          1338   \n",
       "1       3   7057  9810     9568    1762              3293          1776   \n",
       "2       3   6353  8808     7684    2405              3516          7844   \n",
       "3       3  13265  1196     4221    6404               507          1788   \n",
       "4       3  22615  5410     7198    3915              1777          5185   \n",
       "\n",
       "   cluster  \n",
       "0        1  \n",
       "1        1  \n",
       "2        1  \n",
       "3        0  \n",
       "4        1  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使用cluster特征的得分 0.666666666667\n",
      "不使用cluster特征的得分 0.64367816092\n"
     ]
    }
   ],
   "source": [
    "# 划分训练集测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=24)\n",
    "\n",
    "clf = RandomForestClassifier(random_state=24)\n",
    "clf.fit(X_train, y_train)\n",
    "print \"使用cluster特征的得分\", clf.score(X_test, y_test)\n",
    "\n",
    "# 移除cluster特征\n",
    "X_train = X_train.copy()\n",
    "X_train.drop(['cluster'], axis=1, inplace=True)\n",
    "X_test = X_test.copy()\n",
    "X_test.drop(['cluster'], axis=1, inplace=True)\n",
    "clf.fit(X_train, y_train)\n",
    "print \"不使用cluster特征的得分\", clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "只使用cluster特征的得分 0.724137931034\n"
     ]
    }
   ],
   "source": [
    "# 直接用cluster特征对region进行预测\n",
    "only_cluster = X['cluster'].values.reshape(-1, 1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(only_cluster, y, train_size=0.8, random_state=24)\n",
    "\n",
    "clf = RandomForestClassifier(random_state=24)\n",
    "clf.fit(X_train, y_train)\n",
    "print \"只使用cluster特征的得分\", clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：**\n",
    "\n",
    "使用聚类特征cluster的模型对测试数据的Region预测的更为准确。\n",
    "\n",
    "这说明了cluster本身是含有一定信息量的，且该特征与Region特征之间也存在着一定的相关性。（比较诡异的是，我试着只用cluster对region进行预测，发现平均准确率竟然比用所有特征的得分还要高...增加特征真的会降低准确率么）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化内在的分布\n",
    "\n",
    "在这个项目的开始，我们讨论了从数据集中移除`'Channel'`和`'Region'`特征，这样在分析过程中我们就会着重分析用户产品类别。通过重新引入`Channel`这个特征到数据集中，并施加和原来数据集同样的PCA变换的时候我们将能够发现数据集产生一个有趣的结构。\n",
    "\n",
    "运行下面的代码单元以查看哪一个数据点在降维的空间中被标记为`'HoReCa'` (旅馆/餐馆/咖啡厅)或者`'Retail'`。另外，你将发现样本点在图中被圈了出来，用以显示他们的标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6CC56Dn62ArodmHBzyZB4iwtK81VUwoSbofRc72tFZdAtEhGRLAgyNa4NcBQw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e496a70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 根据‘Channel‘数据显示聚类的结果\n",
    "vs.channel_results(reduced_data, outliers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 问题 12\n",
    "\n",
    "*你选择的聚类算法和聚类点的数目，与内在的旅馆/餐馆/咖啡店和零售商的分布相比，有足够好吗？根据这个分布有没有哪个簇能够刚好划分成'零售商'或者是'旅馆/饭店/咖啡馆'？你觉得这个分类和前面你对于用户分类的定义是一致的吗？*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**回答：**\n",
    "\n",
    "从原数据类别所画的图中可以看到，两个分类的混叠程度还是挺厉害的，每个聚类都有不少样本点深入了对方的地盘，你中有我，我中有你，并没有一个特别明确的分界线来划分零售商与旅馆/饭店/咖啡馆。\n",
    "\n",
    "因此，K-Means所硬性划分出的左右两个分类区域与原数据相比，虽然整体趋势是对的，还是在具体点上仍然有一定差距。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 疑问1解答 - 针对混叠数据的聚类划分：GMM vs K-Means的性能差异\n",
    "\n",
    "Review中说：“你被给的图误导了。这里应该把概率plot出来，就更清楚了。GMM就是用来处理混叠数据的。“"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 既然说GMM是专门用来处理混叠数据的，那么为什么画出来的聚类划分图中并没有看到像cluster.csv数据中交错的划分情况呢？或者说到底如何画出GMM的混叠划分呢？\n",
    "\n",
    "    - 回答：不管你用哪种方法，plot出来的图，都是非黑即白两种颜色。既是GMM有概率隐含在点中，我们也看不出。如果不用predict，用predict_proba，把每个点以颜色深浅把概率显示在图上，会更有参考意义。\n",
    "\n",
    "\n",
    "- 在更新回答问题4的计算中已经显示，以cluster.csv中真实的标签数据（Ground Truth）作为参照，针对同样的数据，KMeans的聚类划分准确度其实是高于GMM的，同时，在移除相同的异常点前后，GMM比KMeans还要敏感。这两个现象和上面的说明是直觉上相悖的，不知道该如何解释？我们已经可以看到真实数据中两个分类是相当混叠的，那为什么善于处理混叠数据的软判决GMM会比K-Means还要差呢？\n",
    "\n",
    "    - 回答：这里的确与直觉相反。有些时候简单既是美。可能的原因是GMM在引入概率，不断修复概率的过程当中带来了太多的uncertainty。还不如Kmeans干净利落。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 0 0]\n",
      "[[ 0.14702986  0.85297014]\n",
      " [ 0.19921407  0.80078593]\n",
      " [ 0.2145795   0.7854205 ]\n",
      " [ 0.99416111  0.00583889]\n",
      " [ 0.70209212  0.29790788]]\n",
      "   Dimension 1  Dimension 2\n",
      "0    -1.757983     0.009711\n",
      "1    -1.788665    -0.812251\n",
      "2    -1.883353    -1.599135\n",
      "3     1.155265    -1.405201\n",
      "4    -0.784786    -2.394294\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x14786190>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IioCBEBH5xs1YiYiIqCgYCBGRL9yMlYiIiIqEgRAR+cLNWImIiKhIGAgRkS/cjJWIiIiK\nhIEQEfnCzViJiIioSLiPEBH5xs1YiYiIqCgYCBFRINyMlYiIiIqApXFERERERFQ6DISIiIiIiKh0\nGAgREREREVHpMBAiIiIiIqLSYSBERERERESlw0CIiIiIiIhKh4EQERERERGVDgMhIiIiIiIqHQZC\nRERERERUOgyEiIiIiIiodBgIERERERFR6TAQIiIiIiKi0mEgREREROVVqwFTU9ZXIioVBkJERERU\nThMTwOAgMDpqfZ2YyHpERJQiBkJERERUPrUaUK0C8/PA7Kz1tVplZoioRBgIERERUflMTwPd3Y2P\ndXVZjxNRKTAQIiIiovIZGgLOnm18bHHRepyISoGBEBEREZVPpQKMjwN9fcDAgPV1fNx6nIhKoTPr\nARAREVELtZpVqjU0xAl6EsbGgJERXmOikmJGiIiIyETsaJaOSgUYHmYQRFRCDISIiIhMw45mRESJ\nYyBERERkGnY0IyJKHAMhIiIi07CjGRFR4hgIERERmYYdzYiIEseucURERCZiRzMiokQxECIionIz\nuUV1pWLemIiICoKlcUREVF5sUU1EVFoMhIiIqJzYopqIqNQYCBERUTmxRTURUakxECIionJii2oi\nolJjIEREROXEFtVERKXGrnFERFRebFFNRFRaDISIiKjc2KKaiKiUWBpHRERElGe1GjA1xY6HRAEx\nECIiIiLKK+6FRRQaAyEiIiKiPOJeWESRMBAiIiIiyiPuhUUUCQMhIiIiojziXlhEkTAQIiIiIsoj\n7oVFFEnm7bNFpAPAYQAnVPXqrMdDRERElBvcC4sotMwDIQC7ARwHMJD1QIiIiIhyh3thEYWSaWmc\niFwA4BcAfCDLcRARERERUblkvUZoP4DfBbCS8TiIiIiIiKhEMguERORqAP+hqkfaPO8mETksIodr\n7ItPREREREQxyDIj9LMAflFEpgF8GMDPi8hfND9JVe9U1R2quqPC+lciIkpCrQZMTXEjSiKiEsks\nEFLVN6nqBao6BOBVAD6vqr+W1XiIiKikJiaAwUFgdNT6OjGR9YiIiCgFWa8RIiIqlNpcDVMnplCb\nY2YhF2o1oFoF5ueB2Vnra7XKzBARUQkYEQip6t9yDyEiyruJhycwuH8QowdHMbh/EBOPMLNgvOlp\noLu78bGuLutxIiIqNCMCISKivKvN1VB9oIr5pXnMLsxifmke1UNVZoZMNzQEnD3b+NjiovU4EREV\nGgMhIqIYTJ+cRndHY2ahq6ML0yensxkQ+VOpAOPjQF8fMDBgfR0f5+aUREQl0Jn1AIiIimBo4xDO\nLjdmFhaXFzG0cSibAZF/Y2PAyIhVDjc0xCCIiqlW4z1O1IQZISKiGFT6KxjfOY6+zj4M9Aygr7MP\n4zvHUennhCNTfttiVyrA8DAniFRM7IxI5EpUNesx+LZjxw49fPhw1sMgImqpNlfD9MlpDG0cYhCU\ntYkJqwNcd7e1Dmh83Mr+EJVJrWYFP/Pzq4/19QEzMwz8qbBE5Iiq7mj3PGaEiCJiu2RyqvRXMLxp\nmEFQ1tgWm8jCzohELTEQIoqA7ZKJDJX25M9vCR5R2tgZkaglBkJEIbFdMpHB0pz8cf0FmYydEYla\nYiBEFBLbJRMZLK3JH0vwKA/Gxqw1QZOT1leulSMCwPbZRKGxXTKR4dJoi22X4DkXotslePzEnUxS\nqfCeJGrCjBBRSGyXTJQDSbfF5voLIqLcYkaIKIKxLWMYuXiE7ZKJysouwatWrUzQ4iLXXxAR5QQD\nIaKIKv0VBkBEZZZGCR4REcWOgRAREVFUXH9BRJQ7XCNERERERESlw0CIiIiIiIhKh4EQERERERGV\nDgMhIiIiIiIqHQZCVAi1uRqmTkyhNsfd3ImIiIioPQZClHsTD09gcP8gRg+OYnD/ICYemch6SERE\nRERkOAZClGu1uRqqD1QxvzSP2YVZzC/No3qoyswQEREREXliIES5Nn1yGt0d3Q2PdXV0YfrkdDYD\nIiIiIqJcYCBEuTa0cQhnl882PLa4vIihjUPZDIiIiIiIcoGBEOVapb+C8Z3j6Ovsw0DPAPo6+zC+\ncxyVfu7wTkREREStdWY9AKKoxraMYeTiEUyfnMbQxiEGQURERETUFgMhKoRKf4UBEBERERH5xtI4\nIiIiIiIqHQZCRERElKxaDZiasr4SERmCgRARERH5FzSomZgABgeB0VHr6wQ3vSYiMzAQIiIiIn+C\nBjW1GlCtAvPzwOys9bVaZWaIiIzAQIiopGpzNUydmEJtjhMSIvIhTFAzPQ10N256ja4u63Eioowx\nECIqoYmHJzC4fxCjB0cxuH8QE4+wVIWI2ggT1AwNAWcbN73G4qL1OBFRxhgIESXE1IxLba6G6gNV\nzC/NY3ZhFvNL86geqho3TiIyTJigplIBxseBvj5gYMD6Oj5uPU5ElDEGQlR6SQQsJmdcpk9Oo7uj\n8VPdro4uTJ+czmZARJQPYYOasTFgZgaYnLS+jo2lM14ioja4oSqV2sTDE6g+UEV3RzfOLp/F+M5x\njG2J9j9pZ8ZlfmkeAFA9VMXIxSNGbPo6tHEIZ5cbP9VdXF7E0MahbAZERPkxNgaMjFjlcEND/jM7\nlQqzQERkHGaEqLSSKhEzPeNS6a9gfOc4+jr7MNAzgL7OPozvHDciSCOiHKhUgOFhBjZElHvMCFFp\n2QGLnbUBVgOWKEFBHjIuY1vGMHLxCKZPTmNo4xCDICIiIiodZoSotJIIWGpzNUyfnMa+l+wzPuNS\n6a9geNOwceMiIiIiSgMzQlRadolY9VAVXR1dWFxejBSwNK832nfVPmx75rZUMi52AMbsDhEREZE/\noqpZj8G3HTt26OHDh7MeBhVMHEFEba6Gwf2DDWV2fZ19mNkzk3hgkkTDByKiQqrVgjd6IKLcEZEj\nqrqj3fNYGkelF0eJWFYNErgnEBGRTxMTwOAgMDpqfZ0wZ1sDIsoGAyGiGKTZIMG575HpHeqIiIxQ\nqwHVKjA/D8zOWl+rVevxLMYyNZXNaxNRAwZCRDFIqyV180atR588anyHOiKizE1PA92NHxqhq8t6\nPE3MShEZhWuEiGKUZNOCVuuQ9l21D3s/vbeh4YPXGiE2ViCi0qnVrMBjfvW/n+jrA2Zm0lsrZMIY\niErC7xohdo0jilGlvxIpuPAKUlrte7Ttmdsws2fGV3DDxgpEVEqVCjA+bpXDdXUBi4vWv9MMQOys\nlDMQsrNSDISIMsFAiKiNtDIo7YIUr3VIfgIwZ2MFO5iqHqpi5OIRZoYywuwcUYrGxoCRkey6xg0N\nAWcb/xuOxUXrcSLKBNcIEXloXpMz8Ugy9dx+ur9FXYfExgpmSeveIiKHSgUYHs4mA2Nnpfr6gIEB\n62vaWSkiasA1QkQtpLk30NSJKYweHMXswuy5xwZ6BjB53SSGNw2vGZdbFqFddiHLvY6oEd8LohLj\nXkZEieM+QkQRpZlBCdJ+223fIz/ZhbQ621F7zM4RlViWWSkiasA1QkQtpLk3kB2kVA9VG7q/+QlS\ngqz9GdsyhpGLR7guJWNp3ltERETkjoEQUQtRgpMwwgYprbrJTZ+cdj1G1M52FF3a9xZFwDImIqLC\n4hohojZM7+wV93oT08+3SHitDTcxYbVb7u62un2Nj1udx4iIyGhcI0QUgzxMVONc+8NOZulyW++V\nltpcDVMnpho6E5JDrWYFQfPzwOys9bVatR4nIqJCYGkcUQt52nw0jrU/edlnKA/BqenydG+vkVap\nGje/JCIqPGaEiFz42dfHNFGzC3noZMaMVXR5vLfPmZgABgeB0VHr60SC7z83vyQiKjwGQkQuTAoK\n0iphMr2TWa4n8G2kWaZm0r0dSNqlatz8koio8BgIEbkwJShIMwNi+j5DuZ3At5F2lsuUexu1GjA1\n5T+QsUvVnOxStTDH82NsDJiZASYnra9slEBEVCgMhIhcmBAUZJEBGdsyhpk9M5i8bhIze2aMWjdi\nzAQ+Rlm8xybc26FK3LxK1ZIsmePml0REhcVmCWQM0xbBJ7X5qN/zDLo/UFxM3WeoiHvvZPUeZ7qx\nrrPEzW5EUK0CIyPewYZdqlatWpmgxUXr3/bPBz0eERGVHgMhMoKpXaziDgqCnGcRMyBRZTqBT0CW\n73FmAW+UbmxjY8Dznw985SvAZZcBmzdb5XDs7kZERCFkVhonIheKyEMi8g0R+bqI7M5qLJQt0xbB\nJ7VwPch52lmjfS/ZZ+yanaxkufdO3IwoU0tblG5sExPA9u3A7t3W14kJdncjIqLQsswILQF4vaoe\nFZENAI6IyGdV9RsZjokykFV5kJskM1N+z7N5DPuu2odtz9xWiAwIrVW0LFdbrUrc2mVvWpXUzcyE\nO16epbWXEhFRwXlmhETkJSJSFZGhpsdvjPrCqvqkqh6t//0UgOMANkU9LuWPKSVgSWem/Jyn2xj2\nfnpvKSbIabaQNk2Rsly+hOnG5tU1rkzd3dLcS4mIqOBaBkIichuANwN4LoDPicjrHN9+bZyDqAda\nWwF8Oc7jUj6YUh6UdHtmP+dZ1BbR7XCj1BIK2o2tXQlcGbq7pb2XEhFRwXmVxr0cwFZVXRKRtwP4\nKxF5jqruBSBxDUBE1gP4OIA9qvp9l+/fBOAmALjooovielkyjAnlQWlkptqdpynZsXbi7PDnzILZ\nZYPVQ1WMXDxSngyJyUwpwwpbUlckURpNEBHRGl6lcZ2qugQAqnoSVmA0ICL3Auj2+DnfRKQLVhD0\nl6p6n9tzVPVOVd2hqjsq/A99oWVdHpRWZsrrPE3JjnmJO3tT1ixYLphWhlWmEjg3bAxBRBQrUVX3\nb4h8EsAfq+rfNT3+LgC3qmqkjnMiIgA+COB7qrrHz8/s2LFDDx8+HOVlidoyYT8jE8bQrDZXw7En\nj2Hnh3fizPKZc4/3dfZhZs9M6HHW5moY3D/Y0EQi6jEpBrWaFfw4sw99fVYAwg+lsjMxsTYrVraA\nkIioDRE5oqo72j3PqzTul9weVNW3iMifhR7Zqp8FcB2Ah0Xkq/XHblXVT8VwbKLQTNhQ1IQxONmd\n7NbJuoYgCIje4a+IG6UWAsuwgkujjHBszNos1oRyRSKinGsZCKnqPACIyFWq+umm752I+sKq+gXE\nuNaIyEQmZnaCOl47jhsO3YCF5QXX78exhsmENWLUhGVYwdiZmu5u67olmampVBgAERHFwKtr3E+J\nSAeA2xyPHUxlVEQFUIROaBMPT2Drga2uQVB/V3+sa5iyXiMWl8K0AbebE/T1AQMD1teyNSfwi93c\niIhyyas07lUA/gTAc0TkDwH8E4BtqYyKKOfi6ISWdTbJPge3IKivsw/3XXsftj5ra+4DlzgluSFv\nJliG5Q/LCImIcsmr4cEbVfWFAB4D8EkA5wF4poh8SUQ+ksroiHIqaic0E7JJbucAAD0dPRjfOY4r\nL7ky0yDItMxL0hvyZiap/XlqNWBqqhhZE5YREhHlklcg9GkR+SyACoDzAfwNgBlVfQGA16cxOCKT\nBJl4R9kPyJQJtds59HT04NjNxzLPcpgQKDZjG/AATGvLHRXLCImIcqllIKSqPw/gFwGcBvAcAO8E\ncImI3A+rbI6oNIJOvKPsB2TKhNrtHO7edTc2VzanOo5mpgSKzfKyGW7mirqepux7HBER5ZDXGiGo\n6ryIPK6qfwIAInIMwG8AeFEagyMyQdj1PmE7oZk0oTaxm5sdKDr3HYrawjsObAPuU5HX07CbGxFR\nrngGQsC5zJDtDlWtAfh4ckMiMkuUiXeY/YBMm1CbtqeRSYFiMxMDR+NwPQ0RERmibSDkpKrjSQ2E\nyFRJTLzbdYTjhLo10wLFZqYFjsax19NUq1YmaHGR62lsaWzISkRE54iqZj0G33bs2KGHDx/OehhU\nQhOPTKyZeIdtGFC4FssZSaK9eNYty0uFk/5GaW7IStngPU+UGhE5oqo72j6PgRCRP3FMkmtzNQzu\nH2wos+vr7MPMnpk1x+SkPF0MUCkztZrVPc+5bqqvz2q6wAlzMTDQJUqV30DIq322faBf8vMYUdFV\n+isY3jQcKSjx2xHOxPbQRWZqJzqKkcn7FtkNJJzsBhKUf3nrlGjy7wpRzNoGQgDe5PMxImrDz3qj\n47XjuOEiOCqaAAAgAElEQVTQDYWalJu2+WkzU1qWU0JM37eIDSSKLU+Brum/K0QxaxkIichLReR2\nAJtE5H2OP/cAWEpthEQF0m5/oYmHJ7D1wFYsLC80/FyeJ+V5yG6Z3ImOIsrDp/HckLXY8hLo5uF3\nhShmXhmh7wA4DOAMgCOOPw8AeEnyQyPKjyAZj7EtY5jZM4PJ6yYxs2fm3DoUuzyrOQgC8jspz0vJ\nWZQNcMlwefk0nhuyFldeAt28/K4Qxahl+2xV/RqAr4nIX6nqYopjIsqVMIvs3Vosu+1XBAA9HT25\nnZSbuvmpm0K2LGeXKvM+jfd6T7gha3GNjQEjI2b/Ppr2u0KUAj9rhC4Tkc+KyDdF5Fsi8m0R+Vbi\nIyPKgTgzHm7lWT0dPTh287HYu5eltWYnbyVncTTEMAZr/S2VCrBvH9DTA2zYkO2n8aa8J1wMn41K\nBRgeNjMIAhozV+vXW78z+/aZO16iGPgJhMYB/CmAFwIYBrCj/pWo9OJcZO9WnnX3rruxubI5ptFa\n0lyzw5KzjLDWf9XEBLB372rb4n37sik7M+U9MSUYIzONjVm/I4uL1u/M3r28R6jQ2u4jJCJfVtWf\nSmk8nriPUH4VdU+cIPsCBTlmUtcqifH6fd0ivv9JC33dpqasie7s7OpjAwPW+pPhknyOVasBx44B\nu3aZsT+PCe+JafsVsXQznCSvm2n3CFFIse0jBOAhEfljEflpEdlm/4lhjFQwrcqt8tA1LKwkMh5J\nlmdl1Sa6UCVnKYn0e1P2Wn8763HNNY0TOiC7xd8mvCcmLYZPMzNVpFLApK+bSfcIUQr8ZIQecnlY\nVfXnkxlSa8wImatVw4CsMhBpy0vGw8/7Yfq5mD6+OMTye2PvZN/VZU24y7KTvdsn2k5Zfrqd9Xti\nyqf9aY7DvuZ2aaSJvwd+MzxpXDdT7hFTMYuZG7FlhFT1xS5/Ug+CyFxeDQPKslFlXjIefvYxMjl7\nZ/r44hLL701Z2zG7faINAP392bctbvWexJmx8DpWEm2cw4w9rayDKeuyvATJ8KRx3fLS6jsLXF9X\nSH4yQj8M4DYAz1bVl4rIpQB+WlXH0xigEzNCZpo6MYXRg6OYXVitfR/oGcDkdZMY2jhUioxQ3rhl\nVUzP3pk+vjiV6Vxj5/aJdm8vcOgQsHWreRO6ODMWfo8V16faYceeVtbBhHVZXoJehzSzNcx8NGKm\nLHfiXCN0D4DPAHh2/d/fBLAn/NCoaLxaJLNrmJncMlimZ+9MH1+c2v7eFGnNQ9zcPtG+6y7gyivj\nmbDEnb2JK2MR5FhR2jjb53/8ePixx5l18Ho/TFiX5SVohifNbI3prb7TxrVThdVyQ1WH81X1oyLy\nJgBQ1SURWU54XJQj9qSteqiKro4uLC4vNkzaCrlRZQGZvueP6eOLW8vfmzysechaUptXxn3t7cmV\n81Nme3IVdMxxHqsV5/mfOQOsa/osNcjrxfEetXs/7MCheV2WKZP7MIFaHjZmLSLTg2oKzU9p3N8C\neAWAz6rqNhF5AYA/VNXLUxhfA5bGma1oi9iLdj5+TDwysSagjXsz1yjiGl9u31uWZ2QniWsf5zGT\nvjfaNaGI+/XCjKfV65tc5pV1Aw3yj+9VrvgtjfOTEfptAA8A+BER+QcAFQCvjDg+KqBKfyVfk0oP\nrbrgFZ3p2bs4xpfr9zaNT/3Lqt1kOYlrH2fGIsqx/AQKbuff1wesrAA9PelnW4K8H5WKub8fJmR4\nTA4UTWLCe0Wxa5sRAgAR6QTw4wAEwL+o6mLSA3PDjBClgQvViyv3723eGgHkhZ+StyQzLnFORIMe\nK0iDBbfzP3IEOH06/Ykhs6PxYKktFVSczRIA4DIAPwlgG4AxEXl1lMERmaxMi/LLJvfvbfNi6a4u\n6xP5a69lO9ewTQz8Nhnwu1A9zDjiXJge5Fhu537DDVYjBLfjup3/5s3ZLKpnm+fo8tBenChhbQMh\nETkI4D0AXghguP6nbYRFlFdlW5RfJoV4b+29aO69F+jstD7FLfskJsr+HkG6QbXbmynKOLLoBOh2\n7gsLVnbRbeym7U1l2njyhp3QiHw1SzgO4FL1U0OXMJbGUVpMbxpA4RXmvU1zjxST1xCEKZFyng8Q\nT4lVlFKtrMqTvBogsMys+FheSAUWZ2ncIwCeGX1IRPkxtmUMM3tmMHndJGb2zORzokyuCvPeptXO\n1fTd1IN+qt18PpOT8ZRYhf10PcvyJLu8rKdn7feYGfAnz3t6sbyQyFdG6CEAzwfwFQAL9uOq+ovJ\nDm0tZoQoT3LbopnMZ2c0jh4F9u5Nrp1rlp8Y+81CBW2j3Oq5QLSsV9hrlWZmr5Xjx61yuIWF1ceY\nGWivKI0GTM74EoUUZ/vst0cfDlFyTAw4ct2imczWPPnatw/Yti2ZSUxW7bqDTDCDtI32Op/mBf9B\nJ4dh21envVGj23lt3gzcfbe5G4+ayJnJs++natVqr5y362Zye3GihPltn/3DsJokAMBXVPU/Eh1V\nC8wIUTMTA47ct2gmc6WdockiIxT2Nf0ELn6PHaSldPNrhvl0Pa2NGtudFzMD/pmQySOilmJbIyQi\n18Iqi/slANcC+LKIcENVylxtrobqA1XML81jdmEW80vzqB6qojaXba127ls0l0BtroapE1OZ3yuB\npd3lKYs1BGHP0U/baD/n43fNTqu1U2FaYQftfhZmXYqf84qzjXdehF3jk3Ymj4gS4adZwpsBDKvq\na1T11bD2FHprssMias/UgKMQLZoTYkIAMvHwBAb3D2L04CgG9w9i4pHkFv/Hfr5ZTL7SblGc9Dm2\nOx8/gVgSDQ78BiFhm1cUtVVylGYFURqBsNEAUSH4CYTWNZXC/afPnyNKlKkBR6W/gvGd4+jr7MNA\nzwD6OvswvnO89GVxaQYgraSZRUzkfLOafKWZKUjjHL3Ox08gllVQESUAK2IGw08g0ypQiiOY5T5G\nRLnnJ6D5tIh8RkSuF5HrAfwfAJ9KdlhE7ZkccOStRXPSmRpTyhjTyiImer55mHw5J59hPrHP8hz9\nBGJZBRVRArCiZTD8BDJegVJcwWwZywmJCqRt1zhVfYOIXAPghfWH7lTVTyQ7LCJ/xraMYeTiEeO6\nxgFWoGbSeFpJo+GEHYA4G0jYAUia1yitLGLi52t3ebKDjLgXt0dZNO9ckP+DHwAi1qQ7aHvhLDtZ\njY1Z3b9aXYOwHeJsYa5vrQY89VRji2sgWADW7rzypF1Hw3Zd3YqYISOiwPyWuH0RwN8BeAjAPyY3\nHKLgKv0VDG8azkXQYZq0MjWmlDGmlUV0O9+FpQWs714f34sktdFplOM2f0q/uGhNNtPeKDQKO7gE\nvD/pD5u1cl7fiy4C3vWu9tfE/plrrwVWVqwJv1tWx0/2rSgZjHaBTLuMT9EyZEQUip+ucb8Oq2vc\nfwfwSgBfEpEbkx4YESUvrVIxk8oY0yhbdJ5vX2cfAGAd1mH7ndvjWSsUdX1DUusmDhxo/IS+memL\n84MGgUGDiubre+YM8Na3er9W88+cPQt0dgL33tsYgMUZGEdpQJCWdoGMn4xPHspMiShRbfcREpF/\nAfAzqvqf9X//EIAvquqPpzC+BtxHiCheae95ZOLmt0k6XjuOrQe2YmF5tZyp4fqGLUGLsoeJ114y\nUY5bq1kZjjNnWj8n6T2IokhjzyS36+v2Ws77Ynq6/XsS59iDbGYbhvPcgOhlel6/Q2ntz0RExolt\nHyEATwA45fj3KQCPhx0YUdGZ0CLarzCZmijnV7YyxtNnT6O3s7fhsXMZtyif4Idd39Au4xNl3cT0\nNNDTs/bxVmVc7cbZLiMRd9YijU5wbte3+bWa74ujR9PrYpdEW3An57lt2gRccEH0DJZXVo4ZHyJq\nw08gdALWJqpvF5G3AfgSgEdF5LdF5LeTHR5RvoRpmZx14BSkVCxsS+iszzErrdZGXby4PtqEM+z6\nhiTXTQwNWc0RnDo7ga99LdhE1E+AmMT6qDQWz9vXt7d37fcWF4H1LvfF3r3Avn3JdrGzg8qHHgLW\nNU0L4goGs1o/lsSaqDyUDhKRL35K497m9X1VfUesI/LA0jgyWZgyszQ6tsUlbBldns4xCROPTKB6\nqIquji4sLi9a5z9/SfgSNKegpXV+S6jCdjW74ILGCXl3N/DEE8GO0W58SZawpVVKVatZ66luu63x\ntS7xuC/sMjnne+J8nyYnw43dPmfAfX2XfW2BaGVsXmWBQLj7PwtJlw4SUSz8lsb5aZ+dWqBDlGdB\nWyY7O7bZP1M9VMXIxSNGlo6FaQmdt3MMy2vtk2uL91otnuxD0BbTfts+h2ldPT292ibb1tu72s7Y\n7zG8WiLbz+ls+l9X83PCSqu9dKUCvOUtwM03N75WkPvCbUI+M9O4/qZda3VnlsaNnX2yg6wok3+v\nskAgH62r27XkJqLc8dM1boeIfEJEjorIP9l/0hgcUZ4EbRGdVse2uIRpgZ23cwzDT7ngmrVRWbbu\nDbJuIkgJUBylZX6OcfQocOqU93OiSLO9dPNrtbovJicbSwEPHHAvrQSs4zU/v1XpoFuppK2/H7j/\nfmuSH8e6oeZz6+qyXjtPravTWEdGRKnys0boLwHcDeAVAF7u+EM5U9Z1GmkJ2njAlL11/ArTWCFv\n5xhUpH2YslzI7WeyH6aVdNTgrt0xajVrzUyzffvMn0T71XxfuAUiu3e3zooFaXjglaVZWQG2bo13\n8u88txMnrLLJpO7/JNbxcBNWosJpWxoHoKaqDyQ+EkpU2ddppMW1DKoFO7BoXj/ip2QsqzbUQc4P\niHaOeRCmXLBBmBK0NIQtAYqjtMzrGG6lcxs2ANu2BX8dkznvi6mptefc0dF6Qu6nvND5OnapJGD9\nTG8vINIYgMY5+W++5+2SwHZlfEEktY7Hb2kpEeWGn2YJVwAYA/A5AOc2w1DV+5Id2lpslhBO2nvF\nUDBBg5o8BrVF3T+osL9bfvYTCrsHUhRp7PUTF6/rE+TauZ0zYGWE1q2zAhdncwS35/f2Ao895r1W\naHra6lp3+vTacSXZRCKuoMV5Dtu3J3uPZHHvE1Egce4jdAOA5wO4CqtlcVdHGx6lqQzrNPLM7946\ntbkaHnz0Qdx46MZwpVgex026ZLKo+weFKRfMhXYlQG5lc3GVInkdJ8u1VUF4lRVGKTl0WlqyAqF7\n720sLbOf39W1+tyVFSuI9XqN4WFg82b3ksmkyjij7FvkvE+c13Tr1rXPbS7li3qvprmOjIgS5Scj\n9LCqPjel8XhiRiicwn5qXSJ2FmidrMPc4lzD9wZ6BjB53SSGNwVvO5vH7JKJCpnxapUFcMs4dHVZ\nGYqon+r7zQ6Y/Im8V9YKCJ/RevBB4JprgDnH73+rltN5yZz5yTy6ab5Plpase7QV57mz/TVRKcSZ\nEfqSiFwaw5goI4X91LoknAvym4MgoH3zgVYZnygL/cvWeKPd+RYy49UqC+C2eH5xMXpXsSDZAZM/\nkfdqLuD1vXZZiq1brcyOU6u1Onnpbham+YDbfdIcBPX2Aj09a7OGUTJQRFRIfpolvBDAa0Tk27DW\nCAkAVdXnJToyilXQRe6UDj+ZBLcF+QDQ39WPFV3xDGq9Mj5hF/rnLYsUNVuTt/ONlVszh3b7wQDh\n9vUJssjfZO0m927fO3oUuPxy7yxFkIX6eeluFqb5gNt90kzEuqbN652Kco8RUWz8lMYNuj2uqjOJ\njMgDS+OoSPxMsGtzNRx78hh2fWTXmtLG+3/5fmx91lbPzUy9SiLDlEz6OaZJwXbUIIZlpS04y+bO\nnrUyFc6Jd5gyrLyUc/nh1Vyg+Xv79lktwf2et9+ywCQbHLQaU6tmC35/PmzziO5ua71Ud7f3uRbp\nHqPkmVyCS23FVhpXD3g2YrVRwsYsgiCiIvFTlmZv1Hntx67F0vISuju6G0obr7zkSs/JeLsmGWFK\nJr2O6Wdj0TRF2uOnjo1GWnCWzT32GHDPPdEbGOSlEYIfXs0Fmr+3bVuwMjY/ZYG1GnDJJcCRI8nv\nU2U3Krj8cuDSS62vfppAOAUpdXS7T+65x7oP251rke4xSlbQpiaUW34yQrsB/AYAu132fwdwp6re\nnvDY1mBGiIpi6sQURg+OYnZhdZGws+mBWyait6MXh151yDML5OQ3mxEki9PqmEduOoLtd243KnPS\n7hr7wYxQAHF9elr0T2Gbzy/uLEWazQBatfYGks+0RLlPin6PUTTMHBZCnM0SqgB+SlV/X1V/H8AL\nYAVGRBTS0MYhnF1urOF3Nj1wy0R0d3bjvL7zfE/A/WZ8giz0b3XM02dPG5c5aXeN/WCjkQDiamBg\nciOEqNw+ZY4zS5F2MwC3pgy2pJszRLlPinyPUXR5aTZCsfDTLEEALDv+vVx/jIhCsifY1UNVdHV0\nYXF5sWGCHWUS78zwJNEkw+2Ytbla5KDDbfxRxtvuGvvVfL6AlW0KPL5aDTh2zPr71q2chJWNM0ix\nP2muVoGREStjMzISPUuRdjMAr8YZQZszMEuTb0V6//LSbIRi4ScQuhvAl0XkE/V/7wIwntyQiMrB\nK0gJO4lv1Rwg7gxGpb8Sy3j9jj+suAJB+3xDj29iArj++tX/uXZ1AR/8YDn2L3ErBSvKhCmIdkGK\nW4c+INj1SnsC5+z6pgqcObO68WuQrFYZ9vYp8n1ftPcvTDdDyq22a4QAQES2wWqjDQB/r6rHEh1V\nC1wjRGUTx/qdNNezRMnmmDB+L6HH12odRW+vtcA77f+5pjkha54gVavWhCLMhCnvE8laDbjggsZA\npbsbeOKJ1ucTZoKZZrc4W5SucWVYjxFXoGDi70CR3z8Trzf5FnmNkIgMi8hLAUBVj6rq+1T1fQAu\nFJHtMQ3yKhH5FxF5VER+L45jEhVJkPU7JnQ4i7Kx6PTJaXSua0xSZ73OyCnw9bU3yDx2zGrt26yj\nI/2a8zQ7IbmtV7njjnDrV6KOu91mpWlp/uDR64PIsOt9vDrWeV2HKNfIXnOzeXPwtTdFX48R17ot\n5+/ARRcB73pX9vczUOz3j2vJSsGrWcIfAzju8vg36t+LREQ6APwvAC8FcCmAMRG5NOpxiZrV5mqY\nOjEVqG1yHsXRHCBLR588ilNnTzU8Zsr4a3M1PDX/lP/r65y07Nzpvo5ieTndmvO0FtLbE+qHHnIP\nAJ38TJjcxn3jjcCDD6YTRNljCBokNP/M9DTwtKc1Pqevr/X5R5lguk3gvK5D2q2Cndem6Osx4ggU\nmn8HzpwB3vpWM9o6F/39o8Lz+r/UD6nqdPODqvoogPNjeO3LADyqqt9S1bMAPgxgZwzHJTonyb1t\nTAuw8tzhrDZXw97P7F3z+L6X7Mt8/O32c1ozPrdJC2BNfmxdXcBdd6X7SWMan9w695T55V8G5ua8\nn29PmLwCDbdxnzkDXHNN+4lgHMFfmCDB7WfcJoxnz7aeMMY5wfS6Dml3mmu+NpOTxd7bJ473sVV3\nvqTfKz+4N1O+mJIdN4hXINTn8b2neXzPr00AHnf8+4n6Y0SxiGNDzVZM2zzUNrZlDDN7ZjB53SRm\n9sxEajSQJreys/Xd67HtWdsyGpGl+R5a1EWswzrc+8p7W19ft0lLXx/wyU8Cn/mM9efEifQXEyf9\nya1bVzSn3l7gta9dO2GanPQONFp1Jpubaz8RjBr8hQkSWv0MYJ2vMyBeWbHO302YCWarSY7XdUiz\ntKnVtRkZaV3Ol3dxBApe3flMKEPzKsckc3CTWFdegdCkiLxbRM61yhbLHwD4fPJDO/eaN4nIYRE5\nXGMESwEktWYmyQArDlHW6WRlaOMQFpYWGh5bXlnOvCwu1H5OrQKOrVuBK6+0/qT5aak9OQaS/eTW\na0+Z/n7g0CHg9tsbJ0wjI+0DDedEsr9/7bG9JoJRg78wQYLXz4yMAJ2OdXB2A4lW/28LMsH0muR4\nXYc0S5u8rk2R12NEDRTs34He3rXfM6UMrcjvXxEklfktQIbJKxB6PYDnAHhURD4uIh8H8P8A/BiA\n347htU8AuNDx7wvqjzVQ1TtVdYeq7qjwF4wCSGrNjAlNCYpm8luTWMHKuX93SZcRZX2h7iGTSkWa\nJ8dAcp/cen1qvbJiBYJA44TJb6BhTyTvu2+1PbPNayIY9b0IEyR4/UyYwMrPBLPVJMdeR+V1HdK8\nX8u8niRqoDA2ZnWZfOc7zfhvC+VLEpnfgmSY2rbPFpHnAPiv9X9+XVW/FcsLi3QC+CaAK2AFQFMA\nfkVVv97qZ9g+m4KaeGRizd42UcvFTG/z7Edcm5bGNRaTr2foeyjr1qtZtLW12wQD1uv29gIirdsF\nhxljmPbQUd6LMK/X6meSek+mpqzJyOxs4+P9/VYQ6nz9Vtchrfu1+drs2wds28YWxUFk/d8Wyp+4\n/9uTg7bpfttn+9pHKCki8jIA+wF0ALhLVd/t9XwGQhRGEpP+JAKstMS9aWlUUyemMHpwFLMLq5O4\ngZ4BTF43ieFNw5mNy8mkwNE3t8nxwICVDRpO8LoG3VMmSmATZt8aP8dtPl6YiWern0lin59We1XZ\nDJugoFaz2so/9BCwfz/Q09O4v05eJ/p5HTeVQ5z/7cnq/y8B5CIQCoqBEJkkj5NjE7Mvx2vHsfXA\nViwsr64RynpMhRDkE7u4JnBhjxPm5+LezT7u43lJYsJsj3/durXd+twmKFlO2u2xNgdufX1Whmjv\n3nTehzilef8QhRXnf+sLkhFqs8kDEbXi1ZTAtNbaNtPWN008PIHtd27Huvp/ivo6+3LV9ttoftd+\nxFXnHeU49voJwN/C27gX/rY63vHjySwETmJhuXMdVfOi+uZ1OEE354xzQbTzWjfr6AB2706vlXdc\n0m5BThRWXP/tMWktbEQtAyEReYbXnzQHSZQn7Vprhw2S4giuTNp01dl9b37ZmhSt6AqO3HQkN2WG\nxmvXrSquCVza+/W4Lfzt7AQ+9alwk0+346laDR7ytBC4UrG6Et51V+sJStDNOeNeEO3VXXBxMb1W\n3nFKswU5kSkK0jbdKyN0BMDh+tfmP6xPo0SYmknxq11r7bD7D8W1b5FJm666Zad6Ontw+uzp1MdS\naF6fAMY1gWt1nGPHksnwuHUfO3UKeN3rwk3W3Y535gywsJD9J/xhsjFeE5Qgm3Mmkelo1V2wtxd4\n73uBpaXGx/PQVa7M3fCo3ArQNr1lIKSqF6vqc+pfm/88J81BUjmYuklpkODMq/Qs7P5Dce9bZMqm\nqyZlp/zKe6C+RlwTOLfjzM8Du3aFz/B4BWTOsowNG1YfP3Wq9YTeK5hoLvPo6VnbpjuLT/jjKDds\nnqAE2Zyz1flGuQ5uJTXvfKfVGvrmm/NZblOgMiGisvG1RkhEzhORy0TkRfafpAdG5WLqJqVBgzOv\nyf2xJ49hnTT+yvlZn5PEuh4TNl01KTvlh6mBeqT1G3FN4JqPY7fNjpLhaReQ2VmP229vDIaAxgm9\n32DCmUU5dmzt96N8wh/mPfKTjQlzXK/NOc+caTzH9evXruWZn7cej6I5Y/WWt6zec3ktt8nruIlK\nrm0gJCK/DuD/AvgMgHfUv7492WFR2Zi2iB8IF5y1mtxPfmsSuz6yC3OLjd2c/GRAipw5MSU71Y7X\nvZBpliiO9RtxTeCcxzl0KFhGJWxAVqkAL3tZ63KqoKVddhZl8+boAaIdpBw4EO49apcls9/7K64A\nLrzQeh2/xsas92ld0xSguYvs6dNr38feXuvxqLxKavJabpPXcROVWKeP5+wGMAzgS6r6YhH5CVgB\nEVFsTJzs28GZs9W0HZx5ZS3Gtoxh5OKRc621AaxpWQ0AvR29vjIgdnDVvG+RyZmTIPsUVforxp6L\nrdW9cODwAdz2hduy2ZPJOcm3P7WvVoGRkXAZnTgmb/ZxarVwGZ6RkeCtXe0gqnl/jErFCkS6uxuz\nGnYw0e74YccDrLZS7uy0yvWA4O+RV5bMrfPaLbdYX2++2d/4brjB2mzVqa+v8dq4vV8iXPtCRIXh\npzTujKqeAQAR6VHVfwbw48kOi8rGxDKpKMGZs/TMLdvV39WPQ6865HvSbHLmxJkRMbXEMSq3e+Hs\n0lnc9oXbsjtXkztVRcnwhPlEvVVWK+oaqDDjcQYpdhDk5Pc98rqG09NWkNVs927/jSkWFtZ+r/na\nRCmdjLPlNhFRQvxkhJ4QkY0A7gfwWRF5CsB3kh0WlVFzJiXrLEFcmRi3SfSKrmDrs7YGHk/W16RZ\nc/bn1p+7NVQWzXRu98KtP3cr3vPF94Q/16gb27lN8s+eBZ56yjp22uU5zecTJaMShltWyytbFDf7\n/J96am0WyilIINbqGrZqeNDd3T7bZQfQzePr6XG/NiMjwP33W3/futXftePmokSUE6LNNcFeTxa5\nHMDTAXxaVVu0nUnOjh079PBhdu6mdNXmapGDs4lHJtYEVFlndaKeV22utqbkr7ejFyLS8FhfZx9m\n9szkOhCyOa8ZsLbk0fe5xjVRtI/T1WVNbEWsT+3TnnyaPvENGnQGfb7z/BcWrJKz5kBl/XpgeTm+\na3PgwGo5nM3Pzu5uO8L39FgNIjZvbnxumPc16R3no36AQESlICJHVHVH2+f5CYREZBuAFwJQAP+g\nqkejDzE4BkKUZ3EEVHEJuo7HzdSJKYweHMXswuy5xwZ6BvCGn3kDbvv723wFfSZdkzBCBbhxTxRr\nNWsSu2tXcpPPdq8f5HxMn8gGnfy7nX9Xl1W6Zmeh9u0Dtm1bzQTFdf4HDljlcN3dVsMIv0GWM4C2\ns2Rum+2GuU+npqzGELOr/13AwIBVtjg8HOz8Wo3b1ICbiIwRWyAkIr8P4JcA3Fd/aBeAe1X1XZFH\nGRADIaLo3DI57TIZbgGL13EAtA1w4gjG0tAuWAsczCUxUUxy8hnna2c1kfUbfIWZ/D/4IHDNNcCc\noyPkwABw773Aeec1vmYS5x82sGz3c2HvqaQyQklnmoioUPwGQn6aJYwBGFbVt6nq2wC8AMCvRh0g\nEUv2rz4AACAASURBVGUjaKvyVvvneDW4aLdPUV6aKvjZOyjwnkxJ7EKf5c72fl87aCvruARpMR60\nAcXEBLBzZ2MQBFjnv3VrY6OFuM6/uQlB2AYT7X4u7D2V1OaiJjcHIaLc8hMITQNw7rzWA+BfExkN\nESUuSDe8dgFL2G52Ju4b1SyxYC2JiWKWO9v7fe0sJrJBg48gk3/72GfOND6e5PlH2TcqaBe3KPdU\nEpuLZhnsE1Fh+QmEFgB8XUTuEZG7ATwC4LSIvE9E3pfs8IiCyXRzy4z5Pfcgrcr9BCyBMyIwc9+o\nZokGa0lMFLPc2d7Pa2cxkQ0afASZ/Lsdu7/f6rCWxPn7DercAp6wAVSUeyruzUWzDPaJqLD8tM/+\nRP2P7W+TGQpRNHlZc+ImatOAoOfut1V5XAFL8/mZuEls8xgTD9bi2sQ06WPG9dpptrK2hQk+/Lb9\ndjv2yopVEucm6vkfOwasa/rssnlzWLc1SCMj0TbezfKeapZ2S3YiKrxA7bOzxmYJ1EqYBgCmiBrA\nJX3uXp3R/ARwXudnSte4VmM0se157gVZ3O/nue2e46dDWlivex1wxx2r/37ta4Hbb/ceI9B6vK3O\nxT6H5r1/urqAEyes57ZqJnD//cC114ZrpGF6hz8iohYid40TkY+q6rUi8jCsttkNVPV50YcZDAMh\naqVVK+fJ6yYxvCla16wkJ+txBDFJnrtznM3XwE8Al4cAtd0YTQnWSsdPhzW/XdiSmND77WLmd4yt\nnuf2OrbubuCJJ6zXa9Xl7d57w7VWZ6tqIsqxOLrG7a5/vRrAy13+EBkjqTImP13DoohjHUoa622a\n1wG5NRK48f4b8eCjDzasUcpDU4R2YwyzBooi8rMeJkgjhLjXqwD+1h8FWdfT6nlur2Pr7V19vVZl\ngFu3Bl9bk3SHv6CNG4iIEtIyEFLVJx3P+XdVnVHVGQD/AUDSGByRX0EaAPiVRovnOIKYJM69Hbfg\n4czyGVzz0WswuH8Q7/q/70JtrpaLpgh5GGPsmieipk1M/QQZWbdT9rP+qN0Y7et+7Fjr57m9jtvr\neTUT8Gp64PbeJ3lto3S+y5JpvyNEFAs/XePuBbDi+Pdy/TGi2MTR7S1sK+dW0shmRA1i7Os2cvFI\nrOfejlvwAABzi3OYX5rHWx96Ky7adxEmvz3p6/yCvv9xdgfMIpDMVPNE9HWvM29i6ifIyLqdsp8u\nZl5jdL4PO3euLX2zn+d8nd76ThZ9fe6v5xXwuGXFmu+FAwesyf769clc26z2kooqr8EbEbXVtlmC\niHxVVZ/f9NjXVPUnEx2ZC64RKqYwzQLSWLeR5vqW47Xj+MqJr+CyTZdhc2Wzr5/Jukue3UhgnazD\n3OKc63Ps6wUAx548BgDY+qytDdcv6Hkkdd6lWAvktd7E5mf9SBr8NDlIshGCX2GaNYyMrH0furqA\nzs7W52K/zvr1wOnT0dc7tboXNmwAlpasMY+Px3ttW61jam7cYFKTBj9rwUwaLxEBiKFZguNAnwVw\nu6o+UP/3TgC/papXxDLSABgIFU+YYCPNACCNrmFhA0ETmhDU5mo49uQx7PrIroax2OymDY9+71HX\ncwx6HkmddymCIMCaiF5xBXDqVOvn+O0oloY4usaZoHmMXo0NzjsvnXNxG4NTXx9w5Eh8QZcdxG3f\n7h1UmNakoV3wZtp4iQhAPM0SbLcAuFVEHhORxwG8EcDNUQdIBAQvP0tj3Y5TkHK7MOVaYc8n7rK9\nsKVmlf4KrrzkynOlZc0Wlxexvnt9y3MMeh5uz18n685lm/xoPtekG2IY5ehR7yAIiF4CFedaCj9N\nDpJohBC35jF6NTZwPi/JdSlea48AKxN0+nT0a+ssK9u+3QoaWpUTmlg651XeaOJ4iSiQtoGQqv6r\nqr4AwKUANqvqz6jqo8kPjcog6EL1LLqQ+ekaFnYyHfZ84lzgH0cgYAeM73zxO9Hb0duw1ub02dMt\nzzHoebg9f25xDjs/vNPXuJvP9cCRA2uCtBvuvwHHa8eDXYA8qNWAvXvXPn7jjcE6innhWgp//Kwv\nSvpaOsewYcPa7ye1Jmh83Mo0ua1jyroBhhuv98rE8RJRIH5K43oAvALAEIBO+3FV/YNER+aCpXHF\nFKT8zJSSsLjGFOVn4yjbS+J6NpeZtXuNoOdhP7+5FK/duN3G0dPRg851nWvWOPV09ODuXXcXa/NU\ntxKfDRuAz33OmvBGLS/zu6+OSbzK6tIouWv1GmleS3sMR49agXIWa4KcYzH1HnJ7r0weL1HJxVka\ndwjATgBLAOYcf4hiEaT8LGiHrzi7i7USJUsVpWNZHF3ywpQmtruezRk0+xydr7O0vITJb0+GOo+x\nLWO4/5fvR39Xv+9xA+7nCsC10cPC8kKiJZeZcCv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oMGHK+5FFRzG/gW/QsZlyTYmIUsQN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\nAAAAcocgBAAAACB3CEIAAAAAcuf/AFYz6iKEsk4nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2eb4a6f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9w/sObIDQ0gInnggtLcSjcRoTjUqCZGHo6oK2Nli71v/u6qp0RCJVx9zIkI55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f6ORILAtp2cv6Oa2CkfpiXdRsyKcf7C84tzgwqpqoLbbwfbxuiJcWSbn+Tly9mpp0haSY6sP7J0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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2eaa2b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clusterer = GaussianMixture(n_components=2, random_state=0)\n",
    "clusterer.fit(reduced_data)\n",
    "preds = clusterer.predict(reduced_data)\n",
    "preds_prob = clusterer.predict_proba(reduced_data)\n",
    "\n",
    "print preds[:5]            # 只给出属于哪个聚类的标签\n",
    "print preds_prob[:5]       # 给出属于每个聚类的概率\n",
    "print reduced_data[:5]\n",
    "\n",
    "plt.figure(figsize=(14, 8))\n",
    "for i in np.arange(reduced_data.shape[0]):\n",
    "    data_color = 'red'\n",
    "    if preds[i] == 1:\n",
    "        data_color = 'green'\n",
    "    plt.scatter(reduced_data.loc[i, 'Dimension 1'], reduced_data.loc[i, 'Dimension 2'], \n",
    "                s=20, color=data_color)\n",
    "plt.title('Gaussian Mixture Model Final Prediction')\n",
    "plt.xlabel('Principal Component #1')\n",
    "plt.ylabel('Principal Component #2')\n",
    "\n",
    "plt.figure(figsize=(14, 8))\n",
    "for i in np.arange(reduced_data.shape[0]):\n",
    "    plt.scatter(reduced_data.loc[i, 'Dimension 1'], reduced_data.loc[i, 'Dimension 2'], \n",
    "                s=20, color='red', alpha=preds_prob[i, 0])\n",
    "    plt.scatter(reduced_data.loc[i, 'Dimension 1'] + 0.05, reduced_data.loc[i, 'Dimension 2'], \n",
    "                s=20, color='green', alpha=preds_prob[i, 1])\n",
    "plt.title('Gaussian Mixture Model Prediction (Using Probability as Transparencey)')\n",
    "plt.xlabel('Principal Component #1')\n",
    "plt.ylabel('Principal Component #2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 透明度作为概率值，呈现GMM的优势\n",
    "\n",
    "上图中，我们使用每个点的聚类概率值作为点的透明度参数。可以看到，相比于K-Means，使用GMM的优势是它能够提供额外的概率值，告诉你每个点属于各个聚类的概率是多少。对于处在边界位置的点，属于各个聚类的概率往往相似，因此既有红色也有绿色，而离边界较远的点则只有一种颜色，因为它属于另一个聚类的概率趋近于0，即无限透明。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 疑问2解答 - 衡量聚类好坏的性能度量\n",
    "\n",
    "从这个项目中我主要学到的是用轮廓系数（Silhouette Score）来对没有标签的数据集评估聚类的好坏。我看到sklearn里面介绍了很多针对聚类好坏的评估度量（Clustering performance evaluation），想知道最常用的除了轮廓系数以外，还有哪些是常用的？\n",
    "\n",
    "Review中的回答是：[Sklearn页面](http://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation)要完全消化需要点时间。这些都应用在不同场景下，前面几个都需要ground truth，最后两个可以不用。这样也可以帮你做决定。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 插曲 - whiten\n",
    "\n",
    "在进行PCA处理以及KMeans聚类时，遇到了whiten这个坑。起因是在练习eigenface迷你项目的时候，发现那里面用的PCA必须得加**whiten=True**才能正确计算混淆矩阵，否则会报错且图像识别中也会错误识别的。\n",
    "\n",
    "于是我在没有搞清楚这个参数是干什么的情况下就直接套用到这个项目上。即\n",
    "\n",
    "```\n",
    "pca = PCA(n_components=2, random_state=0, whiten=True)\n",
    "```\n",
    "\n",
    "起初并没有什么异常，但在回答后续问题的时候，就发现不仅在画Biplot的时候箭头会突破天际，而且进行KMeans聚类时，使轮廓系数最佳的聚类个数是3（后来才知道应该是2）。此时我还蒙在鼓里，继续按三类客户(酒店咖啡/零售/大型超市)进行分析(见下个cell，也不是没有道理,写了好多不舍得删了...)，分析的时候就觉得很拧巴，聚类划分图也感觉不太对，感觉是在强行往三个方向上靠。到最后两个问题时，看了UCI资源库网页才发现原来原始数据是带标签的，而且人家只划分了两大类而已，和我预测出来的三类不匹配。然后返回来查问题，最后发现问题的根因出在PCA划分时使用了whiten。后面六七个问题的解答只得推倒重写...\n",
    "\n",
    "sklearn方面对whiten参数的解释是：Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometime improve the predictive accuracy of the downstream estimators by making their data respect some hard-wired assumptions.\n",
    "\n",
    "搜索了一下关于PCA白化方面的信息，了解到PCA Whiten是用于去除特征之间相关性的一种操作，可以进一步的去除冗余，将特征空间上的数据分布从高维椭球面拉为正球型，从而提高后续SVM之类算法的准确性，对相邻像素点冗余度较高的图像处理问题上经常使用白化作为预处理的一步。\n",
    "\n",
    "既然whiten对于相关数据有好的疗效，我不太明白为什么whiten在处理这个客户细分的问题上会对聚类性能有负面影响。在项目最初用特征预测特征的实验上，就可以发现，像Fresh和Deli这两个特征的预测分数是负值，说明无法被其他特征预测，已经是相关性较低的了，而Detergents_Paper是与Milk和Grocery高度相关的。那么whiten为什么不能用呢？\n",
    "\n",
    "换句话说，PCA白化通常应该在什么样的数据集上才应该用？只能靠试轮廓系数么？\n",
    "\n",
    "本项目参考数据：\n",
    "- 关whiten的平均轮廓系数的极值是0.42（在聚类数为2的时候达到）\n",
    "- 开whiten的平均轮廓系数的极值是0.39（在聚类数为3的时候达到）\n",
    "\n",
    "### 关于whiten的使用解答\n",
    "\n",
    "- whiten又被称为sphering，目的是让特征空间中数据点的分布变得更像高维球型（Sphere），这样特征与特征之间的相关性就会更小（想象一下两个特征下数据点的分布，如果形如线则相关性大，如果是一个正圆形的云团则相关性为0）\n",
    "\n",
    "- whiten通常只使用在图像处理领域中。因为图像处理的数据通常特征数量巨大，且相互之间的相关性也巨大，因此需要用whiten来把特征空间中的数据分布形状变换的更圆、更不相关，使用后续模型的准确率才能提高。\n",
    "\n",
    "- 用在特征数量小，且已经有基本不相关特征存在的数据集中，就可能会适得其反，出现上面的这个问题。\n",
    "\n",
    "关于使用PCA + Whiten进行图像处理的案例分析可以参照：[UFLDL](http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/)"
   ]
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    "#### 三类客户的典型特征数值对比整体均值（需求量最大的特征用粗体标注）\n",
    "\n",
    "Data  |Fresh | Milk | Grocery | Frozen | Detergents_Paper | Deli\n",
    "---   |---   | ---  | ---     | ---    | ---              | ---\n",
    "**Segment 0** |\t1052\t| 4725\t| $\\color{red} {8640}$\t| 367\t| 2954\t| 367\n",
    "**Segment 1** |\t$\\color{red} {8651}$\t| 1661\t| 2173\t| 2069\t| 238\t| 610\n",
    "**Segment 2** |\t$\\color{red} {11568}$\t| 7814\t| 10229\t| 2135\t| 3056\t| 1888\n",
    "**Average**   | 12000   | 5769  | 7951  | 3071  | 2881  | 1524\n",
    "\n",
    "**回答:**\n",
    "\n",
    "- Segment 0的典型客户主要需要Grocery，辅助以Milk/Detergents_Paper/Fresh，几乎不需要Frozen和Deli。主营非食物。\n",
    "\n",
    "- Segment 1的典型客户主要需要Fresh，辅助以Grocery/Frozen/Milk，几乎不需要Detergents_Paper和Deli。主营生鲜。\n",
    "\n",
    "- Segment 2的典型客户主要需要Fresh和Grocery，辅助以大量的Milk，Detergents_Paper，Frozen以及Deli。所有特征的需求量都很大。\n",
    "\n",
    "通过三个客户细分在具体特征数值上的相互对比以及与整个数据集平均值的对比，有以下结论：\n",
    "\n",
    "- Segment0和Segment1都**有明确的主营内容**。\n",
    "    - Segment0主要需求是非食物，其Grocery和Detergents_Paper两项数值都超过了数据集的均值。对各项杂物和清洁用品需求量大的企业通常是服务行业，例如酒店。考虑到Segment0还辅助购入一定数量的Milk和Fresh，可能涉及到咖啡馆行业。综合起来，Segment0应该是提供咖啡馆或者in-room dining的酒店企业。\n",
    "\n",
    "    - Segment1主要需求是生鲜，特别是Fresh。由于其各项特征的数值相对数据集均值都较小，可以推测应该属于类似于经营生鲜的零售店企业。\n",
    "\n",
    "\n",
    "- Segment2**没有特别明确的主营内容**，在6个特征中有5个都全面超过了数据集均值，进货量巨大，应该属于大型连锁超市企业。\n"
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    "> **注意**: 当你写完了所有的代码，并且回答了所有的问题。你就可以把你的 iPython Notebook 导出成 HTML 文件。你可以在菜单栏，这样导出**File -> Download as -> HTML (.html)**把这个 HTML 和这个 iPython notebook 一起做为你的作业提交。  "
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